Autonomous laser treatment system for agricultural objects

ABSTRACT

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an agricultural observation and treatment system and method of operation. The agricultural treatment system uses a treatment unit for emitting a laser at agricultural objects. The treatment unit is configured with a treatment head assembly that includes a moveable treatment head with one or more laser emitting tips. A first and second motor assembly are operated by the treatment unit to control the movement of the treatment head. The first motor assembly includes a first motor rotatable in a first rotational axis. A first linkage assembly is connected to the first motor and the treatment head assembly. The first linkage assembly is rotatable by the first motor. The second linkage assembly is rotatable by the second motor.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a non-provisional application and claims the benefitof provisional U.S. Patent Application No. 63/093,106, filed Oct. 16,2020 and provisional U.S. Patent Application No. 63/093,118, filed Oct.16, 2020, which are hereby incorporated by reference in their entirety.

BACKGROUND

Global human population growth is expanding at a rate projected to reach10 billion or more persons within the next 40 years, which, in turn,will concomitantly increase demands on producers of food. To supportsuch population growth, food production, for example on farms andorchards, need to generate collectively an amount of food that isequivalent to an amount that the entire human race, from the beginningof time, has consumed up to that point in time. Many obstacles andimpediments, however, likely need to be overcome or resolved to feedfuture generations in a sustainable manner.

To support such an increase in demand, agricultural technology has beenimplemented to more effectively and efficiently grow crops, raiselivestock, and cultivate land. Such technology in the past has helped tomore effectively and efficiently use labor, use tools and machinery, andreduce the amount of chemicals used on plants and cultivated land.

However, many techniques used currently for producing and harvestingcrops are only incremental steps from a previous technique. The amountof land, chemicals, time, labor, and other costs to the industry stillpose a challenge. A new and improved system and method of performingagricultural services is needed.

SUMMARY

In one embodiment, the agricultural treatment system uses a treatmentunit for spraying fluid at agricultural objects. The treatment unit isconfigured with a treatment head assembly that includes a moveabletreatment head with one or more spraying tips. A first and second motorassembly are operated by the treatment unit to control the movement ofthe treatment head. The first motor assembly includes a first motorrotatable in a first rotational axis. A first linkage assembly isconnected to the first motor and the treatment head assembly. The firstlinkage assembly is rotatable by the first motor. The second motorassembly includes a second motor rotatable in a second rotational axis.The first rotational axis is different from the second rotational axis.The second linkage assembly is rotatable by the second motor.

The treatment unit may spray a fluid controlled by a fluid regulatorwith an operable solenoid to control the release of pressurized fluid. Aflexible tube may fluidly be coupled from the fluid regulator to thetreatment head assembly. The treatment head assembly may include aspraying head and one or more spraying tips to emit the released fluid.A controller may be configured to control the fluid regulator and thefirst and second motors thereby adjusting a position of the moveabletreatment head and emitting a fluid from the one or more spraying tips.In mode of operation, the rotation of the first motor causes thetreatment head assembly to pivot along the first axis, and the rotationof the second motor causes the treatment head assembly to pivot along asecond axis. The agricultural treatment system may determine a firsttarget object to be sprayed with a first fluid emitted from the one ormore spraying tips. The agricultural treatment system may adjust theposition of the spraying head assembly via rotation of the firs and/orsecond motors, and then emit the first fluid, obtained from a fluidsource, at the first target object.

In one embodiment, the agricultural treatment system uses a treatmentunit for emitting laser light at agricultural objects. The treatmentunit is configured with a treatment head assembly that includes amoveable treatment head with one or more laser emitting tips. A firstand second motor assembly are operated by the treatment unit to controlthe movement of the treatment head. The first motor assembly includes afirst motor rotatable in a first rotational axis. A first linkageassembly is connected to the first motor and the treatment headassembly. The first linkage assembly is rotatable by the first motor.The second motor assembly includes a second motor rotatable in a secondrotational axis. The first rotational axis is different from the secondrotational axis. The second linkage assembly is rotatable by the secondmotor.

The treatment unit may emit a laser light generated from a laser lightsource. A fiber optic cable may be configured to receive the generatedlaser light from the laser light source. The fiber optic cable may becouple to a laser emitting tip positioned on the treatment head. Acontroller may be configured to control the laser light source and thefirst and second motors thereby adjusting a position of the moveabletreatment head and emitting laser light from the laser emitting tip. Inmode of operation, the rotation of the first motor causes the treatmenthead assembly to pivot along the first rotational axis, and the rotationof the second motor causes the treatment head assembly to pivot alongthe rotational second axis. The agricultural treatment system maydetermine a first target object to be photoablated with a laser lightemitted from the laser emitting tip. The agricultural treatment systemmay adjust the position of the treatment head assembly via rotation ofthe firs and/or second motors, and then emit the laser light at thefirst target object.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become better understood from the detaileddescription and the drawings, wherein:

FIG. 1 is a diagram illustrating an exemplary environment, according tosome examples.

FIG. 2 is a diagram illustrating an exemplary environment, according tosome examples.

FIG. 3A is a diagram illustrating an agricultural scene within ageographic boundary, according to some examples.

FIG. 3B is a diagram illustrating image acquisition and digitization ofa geographic boundary, according to some examples.

FIG. 3C is a diagram illustrating image acquisition and digitization ofobjects a geographic boundary measured across time, according to someexamples.

FIG. 3D is a diagram illustrating an interface for interacting with adigitized agricultural scene, according to some examples.

FIG. 3E is a diagram illustrating an interface for interacting with anagricultural scene, according to some examples.

FIG. 4 is a diagram illustrating an example agricultural observation andtreatment system, according to some examples.

FIG. 5 is a diagram illustrating a component of an example agriculturalobservation and treatment system, according to some examples.

FIG. 6 is a diagram illustrating an example vehicle supporting anobservation and treatment system performing in a geographic boundary,according to some examples.

FIG. 7A is a diagram of a vehicle navigating in an agriculturalenvironment, according to some examples.

FIGS. 7B-7D are block diagrams illustrating an exemplary method that maybe performed by a treatment system, according to some examples.

FIG. 8 is a diagram illustrating an additional portion of an exampleagricultural observation and treatment system, according to someexamples.

FIG. 9A is a diagram illustrating an example component of anagricultural observation and treatment system, according to someexamples.

FIG. 9B is a diagram illustrating an example component of anagricultural observation and treatment system, according to someexamples.

FIG. 10 is block diagram illustrating an exemplary method that may beperformed by a treatment system, according to some examples.

FIGS. 11A-11H are diagrams illustrating example treatment units,according to some examples.

FIG. 12A is block diagram illustrating an exemplary method that may beperformed by a treatment system, according to some examples.

FIG. 12B is block diagram illustrating an exemplary method that may beperformed by a treatment system, according to some examples.

FIG. 13A is a diagram illustrating an exemplary labelled image,according to some examples.

FIG. 13B is a diagram illustrating an exemplary labelled image,according to some examples.

FIG. 14A is a block diagram illustrating an exemplary method that may beperformed by a treatment system, according to some examples.

FIG. 14B is a block diagram illustrating an exemplary method that may beperformed by a treatment system, according to some examples.

FIG. 15 is block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIG. 16 is a diagram illustrating an example image acquisition to objectdetermination performed by an example system, according to someexamples.

FIG. 17A is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIG. 17B is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIG. 18A is a diagram illustrating capturing an action performed by anobservation and treatment system, according to some examples.

FIG. 18B is a diagram illustrating capturing action and treatmentpattern detection, according to some examples.

FIG. 18C is a diagram illustrating capturing action and treatmentpattern detection, according to some examples.

FIG. 18D is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIG. 18E is a diagram illustrating capturing action and treatmentpattern detection, according to some examples.

FIG. 18F. is a diagram illustrating capturing an action and a treatmentpattern, according to some examples.

FIG. 19A is a diagram illustrating example treatment patterns performedand observed by an example treatment system, according to some examples.

FIG. 19B is a diagram illustrating example treatment patterns performedand observed by an example treatment system, according to some examples.

FIG. 19C. is a diagram illustrating example treatment patterns performedand observed by an example treatment system, according to some examples.

FIG. 20 is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIG. 21A is a diagram illustrating an example vehicle supporting anexample observation and treatment system performing in a geographicboundary, according to some examples.

FIG. 21B is a diagram illustrating an example vehicle supporting anexample observation and treatment system performing in a geographicboundary, according to some examples.

FIG. 21C is a diagram illustrating an example vehicle supporting anexample observation and treatment system, according to some examples.

FIG. 22 is a diagram illustrating an example vehicle supporting anexample observation and treatment system, according to some examples.

FIG. 23 is a diagram illustrating axes of movement, rotation, anddegrees of freedom of a vehicle and components of an observation andtreatment system, according to some examples.

FIG. 24A is a diagram illustrating an example vehicle supporting anexample observation and treatment system, according to some examples.

FIG. 24B is a diagram illustrating an example vehicle supporting anexample observation and treatment system, according to some examples.

FIG. 24C is a diagram illustrating an example vehicle supporting anexample observation and treatment system performing in a geographicboundary, according to some examples.

FIG. 24D is a diagram illustrating an example vehicle supporting anexample observation and treatment system performing in a geographicboundary, according to some examples.

FIG. 24E is a diagram illustrating an example observation and treatmentsystem and components of the observation and treatment system, accordingto some examples.

FIG. 24F is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIGS. 25A-25E are diagrams illustrating example treatment units,according to some examples.

FIG. 26A is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIG. 26B is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIG. 26C is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIG. 27 is a diagram illustrating a treatment system observing anenvironment and performing actions in a geographic boundary, accordingto some examples.

FIG. 28A is a diagram illustrating an example configuration of a systemwith a treatment unit having an example configuration of a fluid sourceand fluid flow mechanisms.

FIG. 28B is a diagram illustrating an example configuration of a systemwith a treatment unit having an example configuration of a fluid sourceand fluid flow mechanisms.

FIG. 28C is a diagram illustrating an example configuration of a systemwith a treatment unit having an example configuration of a fluid sourceand fluid flow mechanisms.

FIG. 28D is a diagram illustrating an example configuration of a systemwith a treatment unit having an example configuration of a fluid sourceand fluid flow mechanisms.

FIG. 28E is a diagram illustrating an example configuration of a systemwith a treatment unit having an example configuration of a fluid sourceand fluid flow mechanisms.

FIG. 28F is a diagram illustrating an example configuration of a systemwith a treatment unit configured with a light source operably connectedto a gimbal mechanism.

FIG. 28G is a diagram illustrating another example configuration of asystem with a treatment unit configured with a light source operablyconnected to a gimbal mechanism.

FIG. 28H is a diagram illustrating another example configuration of asystem with a treatment unit configured with a light source operablyconnected to a gimbal mechanism.

FIG. 29 is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIG. 30 is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIG. 31 is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIG. 32 is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIG. 33 is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

FIG. 34 is a block diagram illustrating an exemplary method that may beperformed by an agricultural observation and treatment system, accordingto some examples.

DETAILED DESCRIPTION

In this specification, reference is made in detail to specificembodiments of the disclosure. Some of the embodiments or their aspectsare illustrated in the drawings.

For clarity in explanation, the disclosure has been described withreference to specific embodiments, however it should be understood thatthe disclosure is not limited to the described embodiments. On thecontrary, the disclosure covers alternatives, modifications, andequivalents as may be included within its scope as defined by any patentclaims. The following embodiments of the disclosure are set forthwithout any loss of generality to, and without imposing limitations on,the claimed disclosure. In the following description, specific detailsare set forth in order to provide a thorough understanding of thepresent disclosure. The present disclosure may be practiced without someor all of these specific details. In addition, well known features maynot have been described in detail to avoid unnecessarily obscuring thedisclosure.

In addition, it should be understood that steps of the exemplary methodsset forth in this exemplary patent can be performed in different ordersthan the order presented in this specification. Furthermore, some stepsof the exemplary methods may be performed in parallel rather than beingperformed sequentially. Also, the steps of the exemplary methods may beperformed in a network environment in which some steps are performed bydifferent computers in the networked environment.

Some embodiments are implemented by a computer system. A computer systemmay include a processor, a memory, and a non-transitorycomputer-readable medium. The memory and non-transitory medium may storeinstructions for performing methods and steps described herein. Variousexamples and embodiments described below relate generally to robotics,autonomous driving systems, and autonomous agricultural applicationsystems, such as an autonomous agricultural observation and treatmentsystem, utilizing computer software and systems, computer vision andautomation to autonomously identify an agricultural object including anyand all unique growth stages of agricultural objects identified,including crops or other plants or portions of a plant, characteristicsand objects of a scene or geographic boundary, environmentcharacteristics, or a combination thereof.

Additionally, the systems, robots, computer software and systems,applications using computer vision and automation, or a combinationthereof, can be configured observe a geographic boundary having one ormore plants growing agricultural objects identified as potential crops,detect specific agricultural objects to each individual plant andportions of the plant, determine that one or more specific individualagricultural object in the real world geographic boundary requires atreatment based on its growth stage and treatment history from previousobservations and treatment, and to deliver a specific treatment to eachof the desired agricultural objects, among other objects. Generally, thecomputer system provides computer vision functionality usingstereoscopic digital cameras and performs object detection andclassification and apply a chemical treatment to target objects that arepotential crops via an integrated onboard observation and treatmentsystem. The system utilizes one or more image sensors, includingstereoscopic cameras to obtain digital imagery, including 3D imagery ofan agricultural scene such as a tree in an orchard or a row of plants ona farm while the system moves along a path near the crops. Onboardlights sources, such as LEDs, may be used by the system to provide aconsistent level of illumination of the crops while imagery of the cropsis being obtained by the image sensors. The system can then identify andrecognize different types of objects in the imagery. Based on detectedtypes of objects in the digital imagery, or the same object from onemoment in time to another moment in time experiencing a different growthstage which can be recognized, observed, and identified by the onsystem, as well as the system associating the growth stage or thedifferent label with a unique individual agricultural object previouslyidentified and located at previous growth stage, the system can apply atreatment, for example spray the real-world object with chemicals pumpedfrom one or more liquid tanks, onto a surface of the agriculturalobject. The system may optionally use one or more additional imagesensors to record the treatment, as a projectile, as it is applied fromthe system to the agricultural object in proximity to the system.

Referring now to FIG. 1 , a diagram of an exemplary network environmentin which example systems and devices may operate is shown. In theexemplary environment, clients 141 are connected over a network 145 to aserver 150 having local storage 151. Clients and servers in thisenvironment may be computers. Server 150 may be configured to handlerequests from clients. Server 150 may be implemented as a number ofnetworked server devices, though it is illustrated as a single entity.Communications and transmissions between a base station and one orvehicles, or other ground mobility units configured to support a server150, and between a base station and one or more control centers asdescribed herein may be executed similarly as the client 141 requests.

The exemplary environment is illustrated with only two clients and oneserver for simplicity, though in practice there may be more or fewerclients and servers. The computers have been termed clients and servers,though clients can also play the role of servers and servers can alsoplay the role of clients. In some examples, the client 141 maycommunicate with each other as well as the servers. Also, the server 150may communicate with other servers.

The network 145 may be, for example, local area network (LAN), wide areanetwork (WAN), networks utilizing 5G wireless standards technology,telephone networks, wireless networks, intranets, the Internet, orcombinations of networks. The server 150 may be connected to storage 152over a connection medium, which may be a bus, crossbar, network,wireless communication interface, or other interconnect. Storage 152 maybe implemented as a network of multiple storage devices, though it isillustrated as a single entity. Storage 152 may be a file system, disk,database, or other storage.

In one example, the client 141 may perform one or more methods hereinand, as a result, store a file in the storage 152. This may beaccomplished via communication over the network 145 between the client141 and server 150. For example, the client may communicate a request tothe server 150 to store a file with a specified name in the storage 152.The server 150 may respond to the request and store the file with thespecified name in the storage 152. The file to be saved may exist on theclient 141 or may already exist in the server's local storage 151.

In another embodiment, the client 141 may be a vehicle, or a system orapparatus supported by a vehicle, that sends vehicle sensor data. Thismay be accomplished via communication over the network 145 between theclient 141 and server 150. For example, the client may communicate arequest to the server 150 to store a file with a specified file name inthe storage 151. The server 150 may respond to the request and store thefile with the specified name in the storage 151. The file to be savedmay exist on the client 141 or may exist in other storage accessible viathe network such as storage 152, or even in storage on the client (e.g.,in a peer-to-peer system). In one example, the vehicle can be anelectric, gasoline, hydrogen, or hybrid powered vehicle including anall-terrain vehicle, a truck, a tractor, a small rover with bogey rockersystem, an aerial vehicle such as a drone or small unmanned aerialsystem capable of supporting a treatment system including visioncomponents, chemical deposition components, and compute components.

In accordance with the above discussion, embodiments can be used tostore a file on local storage such as a disk or solid-state drive, or ona removable medium like a flash drive. Furthermore, embodiments may beused to store a file on an external storage device connected to acomputer over a connection medium such as a bus, crossbar, network,wireless communication interface, or other interconnect. In addition,embodiments can be used to store a file on a remote server or on astorage device accessible to the remote server.

Furthermore, cloud computing and edge computing is another example wherefiles are often stored on remote servers or remote storage systems.Cloud computing refers to pooled network resources that can be quicklyprovisioned so as to allow for easy scalability. Cloud computing can beused to provide software-as-a-service, platform-as-a-service,infrastructure-as-a-service, and similar features. In a cloud computingenvironment, a user may store a file in the “cloud,” which means thatthe file is stored on a remote network resource though the actualhardware storing the file may be opaque to the user. Edge computingutilizes processing, storage, transfer, and receiving data at a remoteserver more local to where most, or a desired portion of the data may beprocessed, stored, and transferred to and from another server, includinga central hub or at each geographic boundary where data is captured,processed, stored, transmitted, and received.

FIG. 2 illustrates a diagram 200 of an example system 100 configured toobserve a geographic boundary in the real-world, for example a farm ororchard, perform object detection, classification, identification, ofany and all objects in the geographic boundary including agriculturalobjects, determine any individual agricultural object that may requirean agricultural treatment based on the agricultural object's growthstage, previous treatments applied, and other characteristics observed,particularly at the point in time of the observation by system 100, andapply a specific treatment to the agricultural object. The system 100can include and object observation and treatment engine that includes animage capture module 104, a request module 106, a positional data module108 for capturing, fusing, and transmitting sensor data related toposition, localization, pose, velocity, and other position relatedsignals to the rest of the system 100, a vehicle module 110, adeposition module 112 for applying a liquid or light treatment on eachindividual object detected and determined to require a treatment, atargeting module 114 for targeting and tracking an identified object inthe real-world based on sensor data and object detection in an imagecaptured of the real-world while a vehicle is moving, and a userinterface (U.I.) module 116. The system 100 may communicate with a userdevice 140 to display output, via a user interface 144 generated by anapplication engine 142. In one example, the deposition module 112 canalso be a treatment module configured to perform non fluid typedeposition treatment including having a mechanical mechanism or endeffector, including mechanical arms, blades, injectors, drills, tillingmechanism, etc., that physically interacts with surfaces or roots ofplant objects or soil.

The system 100 can also include an image processing module 130, eitheron board a vehicle supporting the system 100, part of the system 100,embedded in the system 100, or supported by one or more servers orcomputing devices remote from the vehicle supporting the system 100. Theimage processing module 130 can be configured to process any and allimages or other sensor data captured by the system 100 including featureextraction, object identification, detection, and classification, imagematching, comparing, and corresponding with other images receivedsimultaneously or previously of the same location, labelling uniquefeatures in each of the images, as well as point clouds from variousother sensors such as that of lidars, or a combination thereof.

While the databases 120, 122 and 124 are displayed separately, thedatabases and information maintained in a database may be combinedtogether or further separated in a manner that promotes retrieval andstorage efficiency and/or data security.

FIG. 3A illustrates a diagram 300 a depicting an agricultural scene. Theagricultural scene can be any physical environment in the real-worldused for agriculture such as, but not limited to, a farm or orchard. Theagricultural scene can be contained in a regional geographic boundary ora region without any defined boundaries. The agricultural scene caninclude agricultural objects including a plurality of one or moredifferent types of plants objects having different plant phenologydepending on the season or year on the same agricultural scene. Theagricultural objects can be further observed and categorized based oneach plant anatomy. For example, diagram 300 a can illustrate an orchardhaving permanent plants, such one or more trees 303. These trees 303 canbe permanent trees that can produce crop such as fruit trees or nuttrees in seasonal or yearly cycles for multiple years. The plants canalso be row crops for harvesting where the plants themselves are forharvest. The agricultural objects observed and potentially treated canbe further categorized and identified by the anatomy of the specifictype of tree 303. For example, a plant such as a tree 303 can include atrunk, root, branch, stems, leaves, pedals, flowers, plant pistils andstigma, buds, fruitlets, fruits, and many other portions of a plant thatmake up the plant's anatomy, all of which can be agricultural objects ofinterest for observation and treatment. For example, the tree 303 indiagram 300 a can include one or more agricultural objects 302. Theseobjects can include fruiting flowers or fruitlets that an agriculturaltreatment system can detect and identify in real-time, and perform anaction to treat the flower or fruitlet.

The agricultural scene can also include an agricultural observation andtreatment system 311, supported by an example vehicle 310, performingobservations and actions in the agricultural scene. In one example, thevehicle 310 can travel inside an orchard along a path 312 such that theagricultural observation and treatment system 311 can sense, identify,perform actions on specific agricultural objects 302 in real time, andindex and store the sensed objects 302 and action history, such that theobservation and treatment system 311 can use the previously storedinformation about the specific object 302 that was observed and treatedfor its next treatment upon detection at a later time or a laterphenological stage of the specific object 302. The agriculturalobservation and treatment system 311 itself can be a component orsubsystem of a larger system that can perform computations, store anddisplay information, make decisions, and transmit and receive data froma plurality of agricultural observation and treatment systems performingobservations and actions on a plurality of geographic scenes. The largersystem can manage a mesh network of individual agricultural observationand treatment systems, each performing online, and onboard a vehicle, inone or more geographic regions, and a mesh network of servers and othercompute devices in the cloud or edge to perform real time functions,quasi real-time functions or support functions for each onlineagricultural observation and treatment system, or offline at one or moreservers to analyze data such as sensor data, performance activity,perform training one or more machine learning models, updating machinelearning models stored on one or more of the agricultural observationand treatment systems located at various geographic regions, as well asa plurality of other tasks and storage capabilities that can generallybe performed or maintained offline from the online and real timeperforming treatment systems. Various examples of agriculturalobservation and treatment systems, or components of modular agriculturalobservation and treatment systems are described in further detail belowin this disclosure.

In one example, the agricultural scene can be that of an orchard havinga plurality of fruiting trees planted in rows as illustrated in diagram300 a. The rows can be further partitioned and categorized by zones 304.In this example, the treatment system 311 can perform a differentvariety of chemical treatments with varying treatment parameters, suchas chemicals used, chemical composition, treatment frequency, andperform A/B type testing (A/B testing) on the agricultural scene bydifferent zones of the same plant type, different chemical trials in thesame or different zones or by different individual plant object forharvest, or a combination thereof. The A/B testing for best treatment orbest trial discovery can performed at a microarray level such thatvarying chemical types can be used in real time and varying chemicalcompositions and concentrations can be used in real time. Thesecombinations can go up to over a million different combinations ofdifferent compositions, concentrations, volume, frequency of chemicaltreatment on varying plant varieties at different stages of growth. Inone example, the agricultural observation and treatment system 311 canapply and log each of these different possibilities of varying treatmentparameters and perform A/B testing on each zone, each tree, or each croplevel specificity to determine the optimal treatment process for eachplant or crop type that has not been previously identified in theindustry. For example, as the agricultural observation and treatmentsystem 311 applies different treatment parameters to different objectsin the same geographic region throughout the growing cycle, uponharvest, some fruiting objects will have more desirable traits andcharacteristics as that of others, of the same type of crop. Theagricultural observation and treatment system 311 can determine whichexact object treated and logged from the beginning of the grow cycle forthat particular object of the crop, determine the objects specifictreatment history, including treatments used, concentration, volume,frequency, etc. and determine that the particular treatment processbased on the treatment history of that particular object, that fruitedinto the most desired version of the crop, is the optimal process basedon the AB testing.

Additionally, based on the zone 304 of plants that produces the bestcrops, or the best crop at the individual object or fruit level of eachzone 304, the best crops being based on size, health, color, amount,taste, etc. crop, the agricultural observation and treatment system 311can determine the best method of performing treatment actions, based ona variety of parameters that can be adjusted and customized, and applythe same method of treatment actions on the particular zone 304 thatyielded the best crop, for other crops in a new or subsequent cropcycle. In one example, treating each agricultural objects with adifferent treatment parameter to determine the best method of treating acrop does not have to be partitioned by zones 304. The agriculturalobservation and treatment system 311 can identify, tag, observe, logeach unique agricultural object 302 and treat each agricultural object302 of interest at the individual agricultural object level. Forexample, instead of treating a first zone 304 with a certain amount ortype of chemical of each agricultural object and treating a second zone304 with a different amount or type of chemical, the treatment system311 can treat a first agricultural object 302, such as plant bud, and asecond agricultural object 302, a different plant bud at the same stageof growth as that of the first plant bud, to observe and discover whichbud yields the better fruit.

In one example, the agricultural scene is an orchard having a pluralityof rows and trees planted in each row. The vehicle 310 can autonomouslytravel through each row such that the treatment system 311 can scan oneor more trees 303 along a path of the vehicle to detect variousagricultural objects including agricultural objects 302 for treatment.Once the treatment system 311's sensing system senses a potentialagricultural object, the system 311 can determine whether theagricultural object 302 detected is a new object identified for thefirst time, a previously identified, tagged, and stored object detectedagain, a previously identified, tagged, and stored object detectedagain, that has changed its state or stage of growth in its phenologicalcycle, a previously identified object that has moved or changed inanatomy, or other objects with varying characteristics detected such asstage of growth, size, color, health, density, etc. Once the object isdetected in real time, whether it is of an object previously identifiedand mapped onto a virtual agricultural scene representing the realagricultural scene, the treatment system 311 can determine, based on acombination of determining the agricultural object's identity,phenotype, stage of growth, and treatment history, if any, whether toperform a unique action onto the agricultural object 302 identified. Theaction can be that of a chemical fluid projectile emitted from a deviceas part of the treatment 311 directly onto a portion of a surface of theagricultural object 302. The fluid can be a single liquid projectilesimilar to that of a shape of a water droplet emitted from a watersprayer, a mist or aerosol, a volumetric spray across a period of time,or many other types of fluid that can be emitted from a device discussedlater in this disclosure.

The actions performed by the observation and treatment system 311 can beperformed for the purposes similar to that of many actions typicallyperformed in agriculture. These actions can include soil and fertilizerdeposition, emitting seeds from the treatment system 311 into soil ordirt, treating individual plant objects including thinning, weeding,pollinating, pruning, extracting, harvesting, among many other actionsthat can be performed by a treatment system 311 having a deviceconfigured to sense an individual object and its stage of growth, accessits treatment history, and perform a physical action including emittinga fluid, small object, or shine a light source such as a laser onto theindividual object, physically manipulate the object including removingor moving the object for better sense and treatment of another object,destroying the object, pruning or harvesting the object, or acombination thereof.

In one example, the agricultural scene and geographic region can be afarm where the ground or terrain is partitioned into a plurality of rowswith row crops for planting, growing, and harvesting and the plantsthemselves are harvested, unlike that of orchards where agriculturalobjects are harvested from permanent plants. The observation andtreatment can be observed and performed on the crops themselves, or ofother plants of interest. For example, weeds can grow in the sameagricultural scene as that of a crop of interest such that theobservation and treatment performed by treatment system 311 can be thatof both the crop and the one or more different types of weeds, or justthe weeds. In another example, the agricultural scene can be that of afarm, orchard, or any kind of ground terrain that does not yet have anytrees or crops, but only of dirt and soil.

FIG. 3B and FIG. 3C illustrate diagrams 300 b and 300 c each depicting aportion of a virtual and digitized agricultural scene or area similar tothat of agricultural scene in diagram 300 a. Diagram 300 b can depict avirtual scene generated by an agricultural observation and treatmentsystem similar to that of agricultural observation and treatment system311 or at servers, cloud, or edge computing devices connected to anagricultural observation and treatment system operating and acquiringimages and other perception data of the agricultural scene. The virtualscene can be that of a high definition 2D map, 3D map, or both, of anagricultural scene surveyed, observed, treated, logged, or a combinationthereof, by a treatment system, such as treatment system 311. Thetreatment system 311, having perception and navigation related sensorsand a plurality of modular treatment modules each having its ownsensors, including vision and navigation sensors, compute units,treatment devices or units, illumination devices, can be supported by avehicle 310 that can drive along a path, and can be configured to scanand observe a geographic scene and build a virtual map of the scene.

In general, the vehicle 310 moves along a path in the real world whilethe agricultural observation and agricultural treatment system 311obtains imagery and other sensed readings, including images captured byimage capture devices or point clouds captured by LiDAR's, or aplurality of different sensor readings captured by a plurality ofdifferent sensors, of the external environment. The observation andtreatment system can generate points along the path representingexternal agricultural objects (e.g., plants, crops, trees, debris,patterns, landmarks, keypoints or salient points, patterns, cluster offeatures or patterns that are fixed in space, etc.).

For example, as the vehicle 310 passes by a particular agriculturalobject in the real world, the object determination and object treatmentengine can capture images and reconstruct a digital or virtualgeographic scene representing the geographic scene as illustrated bydiagram 300 b. The diagram 300 b can include a plurality of mapped datapoints representing agricultural objects or clusters of objects,including objects that have been treated, objects for observation,objects indexed for marking location of nearby objects in the overallgeographic scene or the object itself in the global scene, or acombination thereof, as well as landmarks, patterns, regions ofinterest, or a combination thereof. The mapped points depicted byobjects 320, which can include agricultural objects for treatment, withdifferent identifiers based on the phenology or stage of growth of eachindividual objects. The points depicted by objects 320 can be generatedand/or represented by images taken in the real world of the scene,patches of images, lidar point clouds or portions of points clouds, 3dimages modelled by various imaging techniques such as visualconstructions of objects in computer vision, including structure frommotion or 3 d model reconstruction from a single, stereo, multi cameraconfiguration, the cameras being color sensors, black and white sensors,multispectral sensors, or a combination thereof. Multiple images of thesame scene or same object can be combined and analyzed as theagricultural observation and treatment system 311 scans and observes theenvironment multiple times throughout a grow season or year. Eachobject, cluster of objects, or landmarks detected can have a pluralityof layers of images or other sensor readings, such as radar and lidarpoint cloud readings, to form high resolution 2D or 3D reconstructedmodels of the real-world objects detected. In one example, a stereovision system in an image capture module can capture images of objectsin space and superimpose views of the objects captured in the framescaptured in time in stereo into a 3D model of the object. In oneexample, the generated 3D model of the objects detected, includingagricultural objects, with different models at each of its differentdetected and labelled growth stages, can be positioned in the virtualgeographic boundary or geographic scene for a user to scan through a seevia the user interface described below. In one example, a geographicscene can be a fruit orchard having multiple trees 303 and agriculturalobjects 302. The agricultural observation and treatment system 311 canobserve the geographic scene, both in real time via compute unitsdisposed on board the vehicle 310 or edge or cloud compute devices oroffline. The system can generate a digital or virtual map of the scenehaving a plurality of objects 320, clusters of objects 320 that canrepresent a portion of an entire plant 330, for example a tree. Eachobject 320 can include agricultural objects such as fruits, buds,flowers, fruitlets, or other object types of objects that can be treatedby a treatment system, depending on its stage of growth or phenology.

The objects 320 can be digitally indexed objects having a type,identity, stage of growth, and location associated with the objects,which can be represented by individual images, stereo pair images, orportions of images of the real-world equivalent object captured by animage capture device. The object 320 may have an associated geo-graphicdata associated to the object, including position data, orientation andpose estimation relative to the geographic boundary view or relative tophysical components of the agricultural treatment system 400, includingimage sensors, or treatment engines, or relative to other agriculturalobjects. In one example, each of the objects 320 can include images thatare full frame images captured by one or more cameras in theagricultural treatment system. The full frames can be 2D or 3D imageshowing the images captured directly by one or more cameras and/orrendered by the agricultural treatment system 311. The images caninclude images captured a few meters away from the physical surface andposition of agricultural objects in the geographic boundary, which caninclude images of a plurality of individual agricultural objects, thatare potential crops, as well as landmarks including objects or scenery,or other objects of interest including calibration targets and markersor other farming equipment, devices, structures, or machinery typicallyfound on a farm that can be detected for localization of the treatmentsystem and tracking objects in real time and for constructing a map of ascene either in real time or offline. The objects 320 can also includespecific patches within captured full frame images. For example, astereo pair of cameras can each, simultaneously, capture ahigh-resolution image having hundreds of agricultural objects includingtarget objects for treatment, potential objects for observation fortreating at a future time, landmarks, other patterns, etc. The patchescan be identified by the agricultural system by detecting, classifying,identifying and extracting features, and labelling specific portions ofa full image frame, including labelling agricultural objects andspecific stages of growth of agricultural objects. The portion of thefull frame image can be extracted as a patch such that each individualpatch, which itself is a portion of the full frame image, is a visualrepresentation of each individual and unique agricultural object on thegeographic boundary, and can be identified and indexed, associated withits position data, any and all treatment history if any of the objectsdetected are objects detected from a previous trial on the specificmarked and identified agricultural object, as well as timestampsassociated with the image captured and data acquisition, positioncaptured, treatment applied, or a combination thereof. In one example,each object 320 in the virtual construction of a scene depicted bydiagram 300 b can be a point of varying size depending on the actualsize of the object in the real world, or the size of the patchassociated with the object. In another example, each object 320 in thevirtually constructed scene can be represented by the real-world objectvirtual 3d model associated with each of the objects 320. For example,each of the thousands or millions of objects, landmarks, or patterns canbe visually represented as 2d or 3d models of each of the specificobjects in the real world. Thus, a map, either a 2d or a 3d map can begenerated and accessed, visually, illustrating each object, landmark,pattern or region of interest, in the real world such that each object'sand/or landmark's visualization, structure, location, treatment andprediction details can be represented and displayed in the map.

A user interface can be accessed to interface with the digitally mappedagricultural scene such that a user can view images, models, model ofthe specific object, spray history, other data related to agriculturalobjects including predictions related to yield, size, health, disease,etc., of each object observed and or treated associated with itslocation in the real world based on its location in the digital map. Forexample, a cluster of objects 320, including fruits and fruitlets of afruiting tree, located in a specific area in an orchard, for example ina specific zone 304 in the real world, can be mapped to a specific zone340 in the map, such that the cluster of objects 320, accessed by auser, in the map also represents the location of the specific real-worldobjects associated with objects 320 of the virtual map. The userinterface is further described in detail below.

In one example, as illustrated in diagram 300 c, a user interface 350may be accessed. The user interface 350 can show the points representingeach of the objects 320 of the virtual map. The user interface mayprovide for the user selection of the any of the points depicted in thevirtual scene or map. For example, a unique object 353-a, which in thisexample can be an individual and unique fruitlet in an orchard, can bedetected in the orchard by the agricultural observation and treatmentsystem 311. The object 353-a can be detected in one or more images froma system supported by a moving vehicle. A patch of the image can beidentified and extracted depicting just the object 353-a. Andadditionally, a 3D constructed model of the specific fruitlet, that isobject 353-a at the time of capture by the observation and treatmentsystem, can be generated by using one or more computer vision techniquesfrom associated multiple different views of the same object 353-a andassociating each view with location data of object 353-a and motion ofthe image capture device, or by implementing other computer visiontechniques.

Upon selection of a specific point in the user interface, for example apoint of the map representing object 353-a, the user interface 350 candisplay multiple visualizations and information associated with theselected object 353-a. For example, the user interface 350 can displayan image 352 associated with the point selected, as for this caseselecting the point associated with object 353-a. The image 352 can be apixelated 2-dimensional or 3-dimensional image with localizationinformation. The image 352 can also include the specific image patchextracted from the images captured by the treatment system, instead of aconstructed image depicting the shape, size, color, or other uniqueattributes of the object 353-a at the time the observation and treatmentsystem last observed the object 353-a. In one example, the image 352 canbe a pixelated 2d or 3d image that represents a model of the specificobject 353-a in the real world at a stage of growth, or state, detectedby the treatment system. The 2d or 3d model can be generated by usingvarious computer vision techniques by associating multiple views of theobject along with depth and/or motion data of the image capture device.Some objects may be occluded such that an image sensor travelling alonga path guided by a vehicle may not capture the entire view of the objectdetected. For example, the object could be hidden further inside of atree instead of growing closer to the outer surface of a tree. Theobject could have other objects blocking portions of the object fromview such as leaves or other agricultural objects or landmarks ofinterest. In this example, one or more machine learning algorithms canbe applied to process the existing images sensed on the object, whetherthrough a single pass of the image sensor in a single run, capturing aplurality of image frames forming a single video, or in stereo, or byanalyzing image frames from multiple passes, each image frame whethercaptured in a single sequence or multiple sequences from multipletrials, each image frame having at least a portion of a view of theobject. The machine learning algorithm can be used to compensate for theoccluded portions of the object and construct a high resolution 2D imageor 3D model of the object. In cases where the phenological stage of theobject changes in time, the agricultural observation and treatmentsystem 311 can generate multiple constructed images or models depictingdifferent stages of growth of the same object with timestampsassociating which stage of growth was detected and constructed and therelationship between the different constructed images and models of thesame object, for example showing the phenological changes from oneconstructed image of the object to the next constructed image of thesame object. In one example, the computer vision techniques can beperformed using machine learning models and algorithms, either embeddedon board the agricultural observation and treatment system 311, oroffline via edge or cloud computing device. The user interface 350 canalso display multiple views of the same patch associated with a specificobject selected by the user in the virtual map. For example, theagricultural observation and treatment system 311 can capture more thanone view of object 353-a and store all of the different frames thatinclude object 353-a. The system can generate, index and store each ofthe individual patches of images depicting different views of the objectand display it in the user interface 350 as a group of images 354 forthe user when the specific object, for example object 353-a, in thevirtual map is selected.

Additionally, the user interface 350 can display a time series lapse ofthe history of images captured on the same object, the imagespartitioned based on state changes, stage of growth or phenologicalchanges, including bud, leaf, shoot, flower/blossom, fruitingdevelopments, and maturing developments of the same object detected. Forexample, object 353-a can be fruitlet of an apple fruit, detected as anindividual object in the real world with location, position, and/ororientation data, relative to some arbitrary point in the orchard, theexact point in the real world, a relative position to the agriculturalobservation and treatment system 311 such that the treatment systemitself has a location and orientation data in the real-world. And theobject detected at the time can be identified as a fruitlet of afruiting tree. However, the object 353-a will have been a flower earlierin its life cycle at a prior time, and a bud in its life cycle at aneven prior time to flowering, while still being at the same, or close tothe same location and position in the orchard, and location and positionrelative to a defined position of the tree supporting the object 353-a.For example, a portion of the tree, such as a base of the trunk or anarbitrary center point of the tree can be defined with (x₀, y₀, z₀)position data. The object detected can be some (Δx₀, Δy₀, Δz₀) positionrelative to the base or arbitrarily chosen position (x₀, y₀, z₀) in thegeographic scene, zone of the geographic scene, or a particulartree/plant, for example a position (x₁, y₁, z₁). The coordinate systemchosen is just an example wherein a plurality of different coordinatesystems and origin points can be used to locate relative positions andorientations of objects relative to other objects.

In this example, the agricultural observation and treatment system 311may have logged information related to object 353-a before it waslabelled as a fruitlet in its most recent timed log. The system may havedetected the same object 353-a, at the same or near location, when itwas detected with an identifier of a bud associated with the object. Andthen at a later trial detecting the same object 353-a again, but beforeit was detected and labelled with the identifier of fruitlet but afterit was detected and labelled as a bud, another detection with theidentifier, for example, of a flower/blossom. Each of these detectionscan have location position of, or near location position of (x₁, y₁,z₁). In this case, the system can associate the differentidentifications of the same object, based on the objects state changes,or stage of growth or phenological changes, and display, via a series ofviews across time, the state change in sequence in the user interface350. Identifying, storing and indexing, and associating portions ofimages and patches and other sensor readings of objects of the same typewith near or the same locations of the same objects identifiedthroughout time from different trials and identifying with differentstates of the same object in the geographic scene can be performed usingvarious techniques including machine learning feature extraction,detection, and or classification to detect and identify objects in agiven image frame as well as generating keyframes based on the objectsand landmarks detected. The keyframes can be determined to moreefficiently identify and index objects in a frame while reducingredundancy, for example, by identifying common and/or the same landmarksacross multiple frames. The machine learning and other various computervision algorithms can be configured to draw bounding boxes to labelportions of images with objects of interest and background, maskingfunctions to separate background and regions of interest or objects ofinterest, perform semantic segmentation to all pixels or a region ofpixels of an given image frame to classify each pixel as part of one ormore different target objects, other objects of interest, or backgroundand associate its specific location in space relative to the a componentof the treatment system and the vehicle supporting the treatment system.

Additionally, the agricultural observation and treatment system 311 canperform functions to associate portions of image, for example imagepatches of objects, image frames, key frames, or a combination thereoffrom different trials where the agricultural observation and treatmentsystem 311 observed, identified, labelled, and stored information aboutthe same object across multiple states and phenological stages.Additionally, the association of the frames or portion of the frames canbe packaged into a series of image frames that can be displayed insequence as a video displaying the growth, or backwards growth dependingon the direction of displaying the images, of the specific object. Forexample, the series of indexed images or patches of images associatedwith each other throughout time can be displayed in the user interface350 in the video or visual time lapse history 356. In one example, thefunctions can be performed by various computer vision and machinelearning techniques including image to image correspondence includingtemplate matching and outlier rejection, performed by various techniquesincluding RANSAC, k-means clustering, or other feature-based objectdetection techniques for analyzing a series of images frames, or acombination thereof. In one example, the above techniques can also beused to generate key frames of a subsequent trial by comparing framesfrom the subsequent trial with keyframes of one or more prior trials,depending on how many prior trials there are. Additionally, thecomparison and the candidate frames or keyframes from a previous trialthat may be accessed by the agricultural observation and treatmentsystem 311, or at a server offline, to be used to perform comparisons toidentify state and phenological stage change of a same object, such asobject 353-a, can be narrowed down for selection based on location datalogged at the time of capture, pose data logged at the time of capture,or a combination thereof associated with each of the keyframes, orobjects detected in each keyframe. These accessed and selected frames orkey frames in the prior trials, having been selected based on itslocation data associated with the frames or objects detected in theframes, can be used to compare with currently captured frames, orsubsequently captured frames from the prior frames, having similarlocation data associated with the selected frames for key frames tomatch objects that may have different labels, since different states orphenological stages will have different labels due to the states havingdifferent shape, color, size, density, etc. If there is a match, or ifthere is a threshold reached based on the comparison of the accessedframe or keyframe against one or more frames in a subsequently capturedseries of frames, the agricultural observation and treatment system 311can determine that the two, or more, objects of different types andidentifiers associated with each of the objects, are the same object andthat one, with a first phenological stage, changed into the other havinga second label or identifier of a second phenological stage.

In one example, the agricultural observation and treatment system 311can run a machine learning detector on portions of images, to detectobjects of interest in each of the portions of images by performingfeature extraction and generating bounding boxes around an object ofinterest, performing semantic segmentation or performing semanticclassification of each pixel of a region of an image to detect objectsof interest, or a combination thereof. One or more key frames from aprior trial or in a prior frame of captured frames from a subsequenttrial, any trial subsequent to the prior trial, can be propagated intocandidate frames captured in the subsequent trial, for example thesubsequent trial being the most recent trial, or current trial, withimages captured that have not yet been processed and indexed. The frameswith the propagated detections or labels can be used to detect whether amachine learning detection was accurate, whether location dataassociated with the frame is accurate, or detecting other outliers, as athreshold to mitigate false positives and false negatives of featuresdetected but doesn't actually exist or missing but should have beendetected, since these frames will have similar location data associatedto each of the other frames and detecting and corresponding, above acertain threshold, the same features of more than one frame in a sametrial or across frames having the same location from previous trials maygive more confidence that the features detected are real and accurate.

The series of images can be each of the patches displayed in order,similar to that of a video display of images, where a user can view thechanges in state of the object from when it was bud, through variousgrowth stages, including small incremental stages from day-to-day in agrowing season, until fruiting. In one example, a visual time lapsehistory 356 of object 353-a as it was detected as a bud, to a flower, toa fruitlet, and to a fruit, incrementally, can be displayed in the userinterface 350. Additionally, the series of images displayed in thevisual time lapse history 356 of an object, for example object 353-a,can be reconstructed or generated images from combining multiple imagesdepicting the same object or landmark. These images would be machinelearning rendered images generated by associating portions of capturedimages as well as portions of generated pixels of an image generated bya machine learning model to display a better representation of theselected object to the user. This can include generating higherresolutions from up scaling portions of the captured images whenanalyzing portions of the captured images to generate an image displayedto the user or generating views of an object detected that wereotherwise occluded in the captured images. In this example, uponselection of an object in the user interface, the user interface 350 candisplay any captured images 352, where the image itself can containsmaller patches within the image 352 containing views of objects, ordisplay one or more rendered images generated from a plurality ofcaptured images associated with the object selected.

In one example, the visual time lapse history 356 can be used tovisualize the state changes or visualize its real-world growth fromsprouting into crop, or from bud into fruit, depending on the type ofcrop. This would give the effect, in some instances, of displaying agrowth sequence of an agricultural object from a dormant phase, to afully grown crop of the same or substantially the same location. Thelocation would not be the exact since the object will grow and droplower due to its weight or can be externally moved by wind.Alternatively, the visual time lapse history 356 can function as a “timemachine” visualization of the object. The visual time lapse history 356can be viewed in reverse time to view what a currently detected, orotherwise the object's current state in the real world, assuming theagricultural observation and treatment system has captured and detectedthe object in its current or proximately current state, looked like inthe past by visually linking captured sensor readings, including imageframes having views of the object, and displaying them in sequence, suchas a video.

The agricultural observation and treatment system can associatesimilarities from an image frame, or a portion of one or more patcheswithin the frame corresponding with an object of interest captured at afirst time, with another frame captured at a second time that is closein proximity to the first time, for example a day, such that the statechange will be minor and the system can combine location data of theobjects detected in frames from frame to frame across time having a samelocation associated with the object, so the system can have moreconfidence that, for example, an object from a first frame associatedwith a first timestamp is a same object from a second frame associatedwith a second timestamp, because the real world location of both objectsfrom different frames and different timestamps are in proximity witheach other above a certain threshold for the system to determine thatthe two objects are the same. Additionally, the system can determine anyrelationship between the images of the same object, such that one objectturned from one state detected in the first frame to the other statedetected in the second frame. The incremental changes can allow theimage correspondence to reach a certain threshold of confidence suchthat matching an object of a first phenological stage with an object ofa second phenological stage as the same object does not have to rely onits detected spatial proximity in the real-world location associatedwith the object when the object was identified with their respectivecaptured frames and timestamps.

In one example, the user interface 350 can store and display a varietyof information, data, logs, predictions, histories, or other informationrelated to each object. The information can be displayed to a user uponselection of information, or upon selection of the object in aninteractive virtual map. In one example, the user interface 350 candisplay a visualization 358 of various data including data related to anobject's treatment history, observation history, or both. This caninclude information about each of the times the particular objectedselected, for example object 353-a, was detected in the real world andindexed. In one example, the detection of an object across multipleframes or sensor readings in a single trial can be categorized andindexed as a single detection. If a treatment was applied, for example aspraying of a substance, a mechanical interaction with the object with aphysical end effector contacting object 353-a, or any kind of actionother than a treatment that physically affects the object, can be loggedin time and location. As the agricultural observation and treatmentsystem 311 performs multiple trials across period of time, the system311 can associate each observation and/or treatment of a same objectwith each other, and display the information related to observations andtreatments in order. The information can include the type of spray ortreatment used, the length of time of the spray or treatment, the timeassociated with the treatment, timestamp, the phenological stage of theobject detected. This can allow the agricultural observation andtreatment system 311 to determine the treatment parameters per object.For example, the system can determine, due to its indexing andunderstanding of each object in a geographic scene, that in an immediateupcoming trial, a first object, if detected, should receive a treatmentof a first substance, but a similar second object, proximate to thefirst object, if detected, does not need to receive a treatment of thefirst substance, at least during the immediate upcoming trial. Furtherexamples will be provided below in this disclosure. Additionally, theuser interface 250 can display a visualization 360 of data related tofeatures, attributes, and characteristics of each object, or thespecific object selected. The information can include information of theobject relating to its size, color, shape, density, health, or otherinformation related to prediction information relating to yieldestimate, future size, shape, and health, and optimal harvest parametersof the specific object. Additionally, since the actions for treatingeach object, themselves can be sensed, indexed, and stored, a user canaccess each individual treatment action including its parameters such astype, volume, concentration, dwell time or surface contact diameter forfluid projectile treatments on each individual agricultural object orcrop throughout the life cycle of that specific individual crop orobject detected. This would allow a user to determine grow, health, andharvest parameters and data per crop or per object.

In one example, as illustrated in diagram 300 d of FIG. 3D, a user canaccess the user interface 350 and one or more interactive maps through avariety of devices. In one example, the electronic device can be atablet 380 having a user interface including user interface 350 andinteractive virtual map 382. The interactive virtual map 382 can be thatof the virtual maps discussed above. For example. The interactivevirtual map 382 can be that of a virtual map associated with a map of areal-world geographic scene having a plurality of agricultural objectsand landmarks. Because the geographic scene changes over a period oftime, multiple virtual maps can be generated to index each state of thegeographic scene, at a global scene level, such as the broadergeographic level including terrain, topography, trees, large objects,etc., and at a local level, such as that of each agricultural object,including target crop objects. Both the local scene comprising aplurality of agricultural objects and the global scene can be combinedto generate each virtual map. In one example, each virtual map of thesame real world geographic coordinates, or predetermined geofencedlocation, can be associated with each other such that an interactivechanging map can be displayed where one map changing or updating toanother map represents the changing state of the geographical scenechanging across a grow season. This can include plants sprouting, treesgrowing in size, or growing fruits. Each trial performed by theagricultural observation and treatment system 311 can include aplurality of sensor readings, including images captured from imagecapture devices that include 3d structure, location, depth, relativesize to objects in the real world, heatmap, etc., such that a virtualmap of the area sensed in the trial can be generated.

As more trials are performed, more of the geographic scene can bemapped, and thus used to generate a virtual map, or an index ofinformation associated with objects and landmarks of the geographicscene. For example, a first map can be generated to depict a firstgeographic scene captured at a first time with a first set ofcharacteristics, the characteristics including global characteristics,such as number of sprouts, number of trees, amount and color of visibledirt, topography, etc., and including local characteristics, such as percrop object of interest and each of its phenological stages, dependingon the type of geographic scene such as terrain, row crop farm land,orchard, etc., and a second map can be generated to depict the samefirst geographic scene captured at a second time having different globaland local characteristics.

The system can associate the first and second map maps such that thereis a logical link between the first and second map, indexed informationrelated to each of the first and second maps, the indexed and generatedmaps, or the generated interactive virtual maps, such that thegeographic scene having characteristics captured in the first map haveturned into the geographic scene, with the same or similar real-worldgeographic boundary, having characteristics captured in the second map.In one example, the system can generate a single map such that as thesystem performs more trials and senses and captures more of both theglobal and local portions of the geographic scene, and thus mapping moredetails and characteristic changes of the geographic scene from trial totrial, the system can update the same map into one more updated mapshaving updated global and local attributes and characteristics of thegeographic scene across time, instead of or in addition to generatingmultiple maps.

While the description above discussed virtual maps, the discussion canbe applied more generally to indexed information of geographic scenes,including geographic scenes of changing characteristics throughout time,stored in multiple forms and does not necessarily have to be a generatedvirtual map that can be visualized and displayed in a user interface.The real-world geographic scene can be sensed, and indexed in a databasehaving information relating to agricultural objects and landmarks of thegeographic scene with various sensor readings associated with eachagricultural object and landmark, including visual information, locationinformation, etc., such that the information stored in the database canbe used to generate a map. The information of each agricultural objectand landmark can also be used to generate a visualized virtual map thatcan be interfaced with a user on an electronic device.

In one example, the tablet 380 can display an interactive virtual map382 depicting, for example, the most updated map, or the most recentlygenerated map, of a mapped geographic boundary 383. The mappedgeographic boundary 383 can be the most recently captured and sensedstate of a real-world geographic region depicted in diagram 300 a ofFIG. 3A, having a plurality of agricultural objects and landmarkssensed, the agricultural objects being in their current state, andindexed, stored, and mapped as mapped geographic boundary 383. A priormapping, from a previous trial, on the same real-world geographic regioncan also be indexed, stored, and mapped and associated with mappedgeographic boundary 383. For example, agricultural object 370-a can bean individual blossom of an object detected by the agriculturalobservation and treatment system 311 captured in a recent trial. A user,via tablet 380, or any other electronic device, can interact withinteractive virtual map 382 to select a selectable object 370-a in thetablet to view information about object 370-a including any and allviews captured of object 370-a previously, time lapse video and timemachine video of object 370-a's history as it blossomed from a bud, forexample, treatment history, metadata, and crop characteristics of object370-a including prediction type information. The interactive virtual map382 displaying a mapped geographic boundary 383 can have a plurality ofselectable objects 320's to choose from. For example, object 371-a canbe a different object in the same geographic scene having a differenttreatment history as that of object 370-a. The image 352 can be aportion of a larger image captured by one or more image capture devicessuch as a 4K or 8K image frame, where image 352 is a cropped portion ofthe 4K or 8K image frame. Additionally, the image 352 can include morethan one even smaller patches of the image 352 of a specific object,such as image patch 352-1 of image 352 to display a view of virtual ordigitized object 370-a of some real-world object 302 in the real world,for example.

In one example, the selectable object in the virtual map 382 itself canbe an image. Because the virtual map 382 is interactive, the user canzoom in to the specific object in the virtual map 382 to view thespecific object inside interactive virtual map 382. The object zoomedinto can be an animated object depicting the specific object sensed andindexed from the real world, or can be an image patch, cropped from animage capture device, having a view of the object in the image patch.The objects and landmarks indexed in the virtual map 382, are associatedwith a location in the real world. Each animated agricultural object, orrepresentation of the agricultural object can include data representingat least one image captured by an image sensor of the agriculturalobject in the real world, a localization data representing the positionof the agricultural object relative to the geographic boundary itself,the position of the agricultural object relative to the agriculturalobservation and treatment system that captured an image of theindividual agricultural object, or its position relative to otheragricultural objects also with position data associated with theagricultural objects, as well as a timestamp of when the image andlocation data was acquired.

In one example, one or more agricultural object detected in thereal-world will change characteristics, for example phenological stagesor changes in size, such that the system 100 can detect a new feature ofthe agricultural object and assign a label or identifier to theagricultural object that had a different label or identifier previouslyassigned to the same agricultural object having the same or similarposition detected in the geographic boundary. This is due to a portionof a potential crop growing on a plant, for example a lateral, changingcharacteristics due to the growth stage of the plant. As a simplifiedexample, a fruiting tree can have buds on the tree's laterals which canturn into flowers, and then eventually a fruitlet, and then a fruit, forexample. Additionally, each of these features can be associated witheach other, particularly for labeled features of agricultural objectsthat have the same position detected in the real world, or similar imagefeatures from a previous trial of when the system 100 captured images ofthe specific agricultural object, or a combination thereof.

FIG. 3E illustrates a diagram 300 e depicting a user, or human 381,interacting in a real-world environment with an electronic device havinga user interface and interactive virtual map similar to that of the userinterfaces and interactive virtual maps discussed above. A user can havean electronic device with location and image sensing capabilities todetect a location of the device in the real world, the location of thedevice relative to an identified object, the identified object havinglocation data stored in the device or a location accessible wireless bythe device, or a combination thereof. As the user physically navigatesin the geographic boundary, such as an orchard, the user may come acrossone more indexed objects in the real world, that may be in plain view orviewable in real time by the electronic device, such as the tablet 380,a phone or smart device 385, or smart glasses 386, or mixed realitysmart glasses, or any other wearable or holdable device. In one example,the electronic device can be a drone controlled by the user in real timesuch as any drone free can be relayed and displayed in real time to theuser via a device the user is holding with an interactive interfaceincluding a screen.

For example, if the user is near agricultural object 370-a in the realworld, the user can access information stored about object 370-a in theelectronic device, including most recently views of the object,treatment history, or other information and metadata about the object,particularly those discussed above.

Additionally, an augmented reality or mixed reality environment can beaccessed via an electronic device such as a wearable with a display andimage sensors, a phone or table with image sensors, or a combinationthereof. As the user physically navigates the real-world geographicscene, the electronic device's image sensors can capture and detectobjects in its field of view. Each object previously detected, indexed,and stored can be displayed to the user in real time via augmentedreality or mixed reality, as the same objects in the real-world aredetected by the electronic devices in real time. The user can theninteract with the electronic device in a similar way described above. Inone example, an entire virtual map can be augmented on the real-worldgeographic scene so that the user can see information about every objectin the user, or electronic devices, field of view. In one example, avirtual reality environment can be generated such that a user, having avirtual reality device can navigate inside the virtual realityenvironment and interact with each agricultural object and landmarkdisplayed and created in the virtual reality environment. The user canview portions of the entire virtual reality environment changing acrosstime, either forward or backward. For example, a first virtual realityenvironment and scene depicting the geographic scene at a given time canchange, gradually or instantly, to a second virtual reality environmentdepicting the geographic scene at a different time. Each of the objectsand landmarks can also be selectable so the user can view specific viewscaptured of the objects at various times from different trials. FIG. 4illustrates a system architecture of an agricultural observation andtreatment system, or agricultural treatment system 400, or treatmentsystem. The agricultural treatment system 400 can include a robot havinga plurality of computing, control, sensing, navigation, process, power,and network modules, configured to observe a plant, soil, agriculturalenvironment, treat a plant, soil, agricultural environment, or acombination thereof, such as treating a plant for growth, fertilizing,pollenating, protecting and treating its health, thinning, harvesting,or treating a plant for the removal of unwanted plants or organisms, orstopping growth on certain identified plants or portions of a plant, ora combination thereof. In one example, an agricultural observation andtreatment system, described in this disclosure, can be referred to as aportion of a system for observing and treating objects that is onboard amoving vehicle. Performances by the portion of the system onboard themoving vehicle, including computations, and physical actions, can beconsidered online performance or live performance. A portion of thesystem comprising one or more compute or storage components, that areconnected as a distributed system, can be considered the offline portionof the system configured to perform remote computing, serve as a userinterface, or storage. In one example, the agricultural observation andtreatment system is a distributed system, distributed via cloudcomputing, fog computing, edge computing, or a combination thereof, ormore than one subsystem is performing computations and actions live inaddition to the portion of the system onboard a moving vehicle.

The systems, robots, computer software and systems, applications usingcomputer vision and automation, or a combination thereof, can beimplemented using data science and data analysis, including machinelearning, deep learning including convolutional neural nets (“CNNs”),deep neural nets (“DNNs”), and other disciplines of computer-basedartificial intelligence, as well as computer-vision techniques used tocompare and correspond features or portions of one or more images,including 2D and 3D images, to facilitate detection, identification,classification, and treatment of individual agricultural objects,perform and implement visualization, mapping, pose of an agriculturalobject or of the robotic system, and/or navigation applications usingsimultaneous localization and mapping (SLAM) systems and algorithms,visual odometry systems and algorithms, including stereo visualodometry, or a combination thereof, receive and fuse sensor data withsensing technologies to provide perception, navigation, mapping,visualization, mobility, tracking, targeting, with sensing devicesincluding cameras, depth sensing cameras or other depth sensors, blackand white cameras, color cameras including RGB cameras, RGB-D cameras,infrared cameras, multispectral sensors, line scan cameras, area scancameras, rolling shutter and global shutter cameras, optoelectricsensors, photooptic sensors, light detection and ranging sensors (LiDAR)including spinning Lidar, flash LiDAR, static Lidar, etc., lasers, radarsensors, sonar sensors, radio sensors, ultrasonic sensors andrangefinders, other range sensors, photoelectric sensors, globalpositioning systems (GPS), inertial measurement units (IMU) includinggyroscopes, accelerometers, and magnetometers, or a combination thereof,speedometers, wheel odometry sensors and encoders, wind sensor, stereovision systems and multi-camera systems, omni-directional visionsystems, wired and wireless communications systems and networkcommunications systems including 5G wireless communications, computingsystems including on-board computing, mobile computing, edge computing,cloud and cloudlet computing, fog computing, and other centralized anddecentralized computing systems and methods, as well as vehicle andautonomous vehicle technologies including associated mechanical,electrical and electronic hardware. The systems, robots, computersoftware and systems, applications using computer vision and automation,or a combination thereof, described above, can be applied, for example,among objects in a geographic boundary to observe, identify, index withtimestamps and history, and/or apply any number of treatments toobjects, and, more specifically, of an agricultural delivery systemconfigured to observe, identify, index, and/or apply, for example, anagricultural treatment to an identified agricultural object based on itslocation in the real-world geographic boundary, growth stage, and anyand all treatment history.

In this example, the agricultural treatment system 400 agriculturaltreatment system 400 can include an on-board computing unit 420, suchcompute unit 420 computing unit embedded with a system on chip. Theon-board computing unit can include a compute module 424 configured toprocess images, send and receive instructions from and to variouscomponents on-board a vehicle supporting the agricultural treatmentsystem 400 a gricultural treatment system 400. The computing unit canalso include an engine control unit 422, a system user interface, systemUI 428, and a communications module 426.

The ECU 422 can be configured to control, manage, and regulate variouselectrical components related to sensing and environment that theagricultural treatment system 400 will maneuver in, electricalcomponents related to orienting the physical components of theagricultural treatment system 400, moving the agricultural treatmentsystem 400, and other signals related to managing power and theactivation of electrical components in the treatment system. The ECU 422can also be configured to synchronize the activation and deactivation ofcertain components of the agricultural treatment system 400 such asactivating and deactivating the illumination module 460, and synchronizethe illumination module 460 with one or more cameras of the cameramodule 450 or one or more other sensors of the sensing module 451 forsensing an agricultural scene for observation and treatment ofagricultural objects.

The compute module 424 can include computing devices and componentsconfigured to receive and process image data from image sensors or othercomponents. In this example, the compute module 424 can process images,compare images, identify, locate, and classify features in the imagesincluding classification of objects such as agricultural objects,landmarks, or scenes, as well as identify location, pose estimation, orboth, of an object in the real world based on the calculations anddeterminations generated by compute module 424 on the images and othersensor data fused with the image data. The communications module 426, aswell as any telemetry modules on the computing unit, can be configuredto receive and transmit data, including sensing signals, renderedimages, indexed images, classifications of objects within images, datarelated to navigation and location, videos, agricultural data includingcrop yield estimation, crop health, cluster count, amount of pollinationrequired, crop status, size, color, density, etc., and processed eitheron a computer or computing device on-board the vehicle, such as one ormore computing devices or components for the compute module 424, orremotely from a remote device close to the device on-board the vehicleor at a distance farther away from the agricultural scene or environmentthat the agricultural treatment system 400 maneuvers on.

For example, the communications module 426 can communicate signals,through a network 520 such as a wired network, wireless network,Bluetooth network, wireless network under 5G wireless standardstechnology, radio, cellular, etc.to edge and cloud computing devicesincluding a mobile device 540, a device for remote computing of dataincluding remote computing 530, databases storing image and other sensordata of crops such as crop plot repository 570, or other databasesstoring information related to agricultural objects, scenes,environments, images and videos related to agricultural objects andterrain, training data for machine learning algorithms, raw datacaptured by image capture devices or other sensing devices, processeddata such as a repository of indexed images of agricultural objects. Inthis example, the mobile device 540 can control the agriculturaltreatment system 400 through the communications module 426 as well asreceive sensing signals from the telemetry module 366. The mobile device540 can also process images and store the processed images in thedatabases 560 or crop plot repository 570, or back onto the on-boardcomputing system of agricultural treatment system 400. In one example,remote computing 530 component can be one or more computing devicesdedicated to process images and sensing signals and storing them,transferring the processed information to the database 560, or back tothe on-board computing device of agricultural treatment system 400through the network 520.

In one example, the agricultural treatment system 400 includes anavigation unit 430 with sensors 432. The navigation unit 430 can beconfigured to identify a pose and location of the agricultural treatmentsystem 400, including determining the planned direction and speed ofmotion of the agricultural treatment system 400 in real time. Thenavigation unit 430 can receive sensing signals from the sensors 432. Inthis example, the sensing signals can include images received fromcameras or Lidar's. The images received can be used to generate a gridmap in 2D or 3D based on simultaneous visualization and mapping (SLAM)including geometric SLAM and Spatial SLAM techniques, visual odometry,or both, of the terrain, ground scene, agricultural environment such asa farm, etc. The sensing signals from the sensors 432 can also includedepth signals from depth sensing cameras including RGB-D cameras orinfrared cameras, or calculated with stereo vision mounted sensors suchas stereo vision cameras, as well as other signals from radar, radio,sonar signals, photoelectric and photooptic signals, as well as locationsensing signals, from having a global positioning system (GPS) unit,encoders for wheel odometry, IMU's, speedometers, etc. A compute module434, having computing components such as a system on chip or othercomputing device, of the navigation unit 430, or compute module 424 ofthe compute unit 420, or both, can fuse the sensing signals received bythe sensors 432, and determine a plan of motion, such as to speed up,slow down, move laterally, turn, change the rocker orientation andsuspension, move, stop, or a combination thereof, or other location,pose, and orientation-based calculations and applications to align atreatment unit 470 with the ground, particularly with an object ofinterest such as a target plant on the ground. In one example, thenavigation unit 430 can also receive the sensing signals and navigateagricultural treatment system 400 autonomously. For example, anautonomous drive system 440 can include motion components including adrive unit 444 having motors, steering components, and other componentsfor driving a vehicle, as well as motion controls 442 for receivinginstructions from the compute module 424 or compute module 424, or both,to control the drive unit and move the vehicle, autonomously, from onelocation and orientation to a desired location and orientation.

In one example, the navigation unit 430 can include a communicationsmodule 436 to send and receive signals from other components of theagricultural treatment system 400 such as with the compute unit 420 orto send and receive signals from other computing devices and databasesoff the vehicle including remote computing devices over the network 520.

In another example, the navigation unit 430 can receive sensing signalsfrom a plurality of sensors including one or more cameras, Lidar, GPS,IMUS, VO cameras, SLAM sensing devices such as cameras and LiDAR,lasers, rangefinders, sonar, etc., and other sensors for detecting andidentifying a scene, localizing the agricultural treatment system 400and treatment unit 470 onto the scene, and calculating and determining adistance between the treatment unit 470 and a real world agriculturalobject based on the signals received, fused, and processed by thenavigation unit 430, or sent by the navigation unit 430 to be processedby the compute module 424, and/or another on-board computing device ofthe treatment system 900. The images received can be used to generate amap in 2D or 3D based on SLAM, visual odometry including geometry basedor learning based visual odometry, or both, of the terrain, groundscene, agricultural environment such as a farm, etc. The sensing signalscan also include depth signals, from having depth sensing camerasincluding RGB-D cameras or infrared cameras, a radar, radio, sonarsignals, photoelectric and photooptic signals, as well as locationsensing signals from GPS, encoders for wheel odometry, IMUS,speedometers, and other sensors for determining localization, mapping,and position of the agricultural treatment system 400 to objects ofinterest in the local environment as well as to the regionalagricultural environment such as a farm or other cultivated land thathas a designated boundary, world environment, or a combination thereof.The navigation unit 430 can fuse the sensing signals received by thesensors, and determine a plan of motion, such as to speed up, slow down,move laterally, turn, move, stop, change roll, pitch, and/or yaworientation, or a combination thereof, or other location, localization,pose, and orientation-based calculations and applications.

In one example, the navigation unit 430 can include a topography moduleconfigured to utilize sensors, computer components, and circuitryconfigured to detect uneven surfaces on a plane or scene of the terrainwhich allows the topography module to communicate with the rest of thecomponents of the treatment system to anticipate, adjust, avoid,compensate for, and other means of allowing the agricultural treatmentsystem 400 to be aware of uneven surfaces detected on the terrain aswell as identify and map unique uneven surfaces on the terrain tolocalize the vehicle supporting the navigation unit 430.

In one example, the agricultural treatment system 400 includes a cameramodule 450 having one or more cameras, sensing module 451 having othersensing devices, or both, for receiving image data or other sensing dataof a ground, terrain, orchard, crops, trees, plants, or a combinationthereof, for identifying agricultural objects, such as flowers, fruits,fruitlets, buds, branches, plant petals and leaves, plant pistils andstigma, plant roots, or other subcomponent of a plant, and the location,position, and pose of the agricultural objects relative to a treatmentunit 470, camera module 450, or both, and its position on the ground orterrain. The cameras can be oriented to have a stereo vision such as apair of color or black and white cameras oriented to point to theground. Other sensors of sensing module 451 can be pointed to the groundor trees of an orchard for identifying, analyzing, and localizingagricultural objects on the terrain or farm in parallel with the camerasof the camera module 450 and can include depth sensing cameras, LiDAR's,radar, electrooptical sensors, lasers, etc.

In one example, the agricultural treatment system 400 can include atreatment unit 470 with a treatment head 472. In this example, thetreatment unit 470 can be configured to receive instructions to pointand shine a laser, through the treatment head 472, to treat a targetposition and location on the ground terrain relative to the treatmentunit 470.

The agricultural treatment system 400 can also include motion controls442, including one or more computing devices, components, circuitry, andcontrollers configured to control mechatronics and electronic componentsof a vehicle supporting the agricultural treatment system 400 configuredto move and maneuver the agricultural treatment system 400 through aterrain or orchard having crops and other plants of interest such that,as the agricultural treatment system 400 maneuvers through the terrain,the cameras 350 are scanning through the terrain and capturing imagesand the treatment unit is treating unwanted plants identified in theimages captured from the camera module 450 and other sensors fromsensing module 451. In one example, an unwanted plant can be a weed thatis undesirable for growing next or near a desirable plant such as atarget crop or crop of interest. In one example, an unwanted plant canbe a crop that is intentionally targeted for removal or blocking growthso that each crop growing on a specific plant or tree can be controlledand nutrients pulled from the plant can be distributed to the remainingcrops in a controlled manner.

The agricultural treatment system 400 can also include one or morebatteries 490 and one or configured to power the electronic componentsof the agricultural treatment system 400, including DC-to-DC convertersto apply desired power from the battery 490 to each electronic componentpowered directly by the battery.

In one example, the illumination module 460 can include one or morelight arrays of lights, such as LED lights. The one or more light arrayscan be positioned near the one or more cameras or sensors of cameramodule 450 and sensor module 451 to provide artificial illumination forcapturing bright images. The light arrays can be positioned to pointradially, from a side of the vehicle, pointed parallel to the ground,and illuminate trees or other plants that grow upwards. The light arrayscan also be positioned to be pointed down at the ground to illuminateplants on the ground such as row crops, or other plants or soil itself.The light arrays can be controlled by the ECU 422, as well as by asynchronization module, embedded in the ECU 422 or a separate electroniccomponent or module, such that the lights only flashes to peak power andluminosity for the length of 1 frame of the camera of camera module 450,with a matched shutter speed. In one example, the lights can beconfigured by the ECU 422 to flash to peak power for the time length ofa multiple of the shutter speed of the camera. In one example, thelights of the light array can be synchronized to the cameras with a timeoffset such that the instructions to activate the LED's of the lightarray and the instructions to turn on the camera and capture images areoffset by a set time, predetermined time, or automatically calculatedtime based on errors and offsets detected by the compute unit 420, sothat when the LED's actually activate to peak power or desiredluminosity, which will be a moment in time after the moment in time theECU sends a signal to activate the light array, the camera will alsoactivate at the same time and capture its first image, and then both thelights and cameras will be synchronized and run at the same frequency.In one example, the length of time of the peak power of the activatedlight is matched and synchronized with the exposure time of each framecaptured of the camera, or a multiple of the exposure time. In oneexample, the cameras can include

For example, the lights of the light array can flash with turning on,reach peak power, and turn off at a rate of 30 to 1000 Hertz (Hz). Inone example, the lights can flash at 240 Hz to match one or more camerasthat has a rolling shutter speed, global shutter speed, or both, of 240Hz. In one example, the lights can flash at 240 Hz to match one or morecameras that has a rolling shutter speed, global shutter speed, or both,of 30 or 60 Hz. In one example, the lights can reach a peak power of2.0M Lumen with a sustained peak power ON for 250 microseconds with aduty cycle of less than 10%. In one example, the color temperature ofthe light 170 can include the full spectrum of white light includingcool, warm, neutral, cloudy, etc. In one example, the color temperatureof the light can be around 5000K nm to reflect and artificially imitatethe color temperature of the Sun.

In one example, the agricultural treatment system 400 can include atreatment unit 470 with a treatment head 472. In this example, thetreatment unit 470 can include a turret and circuitry, electroniccomponents and computing devices, such as one or more microcontrollers,electronic control units, FPGA, ASIC, system on chip, or other computingdevices, configured to receive instructions to point and a treatmenthead 472, to treat a surface of a real-world object in proximity of thetreatment unit 470. For example, the treatment unit 470 can emit a fluidprojectile of a treatment chemical onto an agricultural object in thereal world based on detecting the agricultural object in an imagecaptured and determining its location in the real world relative to thetreatment unit 470.

The treatment unit 470 can include a gimbal assembly, such that thetreatment head 472 can be embedded in, or supported by the gimbalassembly, effectively allowing the treatment head 472 to rotate itselfand orient itself about one or more rotational axes. For example, thegimbal assembly can have a first gimbal axis, and a second gimbal axis,the first gimbal axis allowing the gimbal to rotate about a yaw axis,and the second gimbal axis allowing the gimbal to rotate about a pitchaxis. In this example, a control module of the treatment unit cancontrol the gimbal assembly which changes the rotation of the gimbalassembly about its first gimbal axis, second gimbal axis, or both. Thecompute module 424 can determine a location on the ground scene,terrain, or tree in an orchard, or other agricultural environment, andinstruct the control module of the treatment unit 470 to rotate andorient the gimbal assembly of the treatment unit 470. In one example,the compute module 424 can determine a position and orientation for thegimbal assembly to position and orient the treatment head 472 in realtime and make adjustments in the position and orientation of thetreatment head 472 as the agricultural treatment system 400 is movingrelative to any target plants or agricultural objects of interest on theground either in a fixed position on the ground, or is also moving. Theagricultural treatment system 400 can lock the treatment unit 470, atthe treatment head 472, onto the target plant, or other agriculturalobject of interest through instructions received and controls performedby the control module of the treatment unit 470, to adjust the gimbalassembly to move, or keep and adjust, in real time, the line of sight ofthe treatment head 472 onto the target plant.

In one example, a chemical selection module, or chemical selection 480,of agricultural treatment system 400 agricultural treatment system 400can be coupled to the compute module 424 and the treatment unit 470. Thechemical selection module can be configured to receive instructions tosend a chemical fluid or gas to the treatment unit 470 for treating atarget plant or other object. In this example, the chemical selectionmodule can include one or more chemical tanks 482, one or more chemicalregulators 484 operable connected to the one or more chemical tanks 484such that there is one chemical regulator for tank, a pump for eachtank, and a chemical mixer 488 which can mix, in real time, chemicalmixtures received from each chemical tank selected by the chemical mixer488. In one example, a vehicle supporting the agricultural treatmentsystem 400 a gricultural treatment system 400, including the chemicalselection module 480, can support one chemical tank 482, a chemicalpump, a chemical regulator 486, a chemical and a chemical accumulator,in series, linking connecting a pathway for a desired chemical or liquidto travel from a stored state in a tank to the treatment unit 470 fordeposition on a surface of an object. The chemical regulator 484 can beused to regulate flow and pressure of the fluid as it travels from thepump to the treatment unit. The regulator 484 can be manually set by auser and physically configure the regulator on the vehicle, orcontrolled by the compute unit 420 at the compute module 424 or ECU 422.The chemical regulator 484 can also automatically adjust flow andpressure of the fluid from the pump to the treatment unit 470 dependingon the treatment parameters set, calculated, desired, or a combinationthereof. In one example, the pump can be set to move fluid from thestorage tank to the next module, component, in the series of componentsfrom the chemical tank 482 to the treatment unit 470. The pump can beset at a constant pressure that is always pressurized when the vehicleand agricultural treatment system 400 agricultural treatment system 400is currently running a trial for plant or soil treatment. The pressurecan then be regulated to controlled from the constant pressure at theregulator, and also an accumulator 487, so that a computer does not needto change the pump pressure in real time. Utilizing a regulator andaccumulator can cause the pressure needed for the spray or emission of afluid projectile to be precisely controlled, rather than controllingvoltage or power of the pump. In one example, the agricultural treatmentsystem 400 agricultural treatment system 400 will identify a targetplant to spray in the real world based on image analysis of the targetplant identified in an image captured in real time. The compute unit 420can calculate a direction, orientation, and pressurization of thetreatment unit 470 such that when the treatment unit 470 activates andopens a valve for the pressurized liquid to pass from the chemicalselection module 480 to the treatment unit 470, a fluid projectile of adesired direction, orientation, and magnitude, from the pressure, willbe emitted from the treatment unit 470 at the treatment head 472. Thepump will keep the liquid stream from the chemical tank 482 to thetreatment unit 470 at a constant pressure, whether or not there is flow.The chemical regulator 484 in the series of components will adjust andstep down the pressure to a desired pressure controlled manually beforea trial, controlled by the compute unit 420 before the trial, orcontrolled and changed in real time during a trial by the compute unit420 either from remote commands from a user or automatically calculatedby the compute module 424. The accumulator 487 will keep the liquidstream in series pressurized to the desired pressure adjusted andcontrolled by the chemical regulator 484, even after the treatment unit470 releases and emits pressurized fluid so that the stream of fluidfrom the pump to the treatment unit 470 is always kept at a desiredpressure without pressure drops from the release of pressurized fluid.

In one example, the chemical can be a solution of different chemicalmixtures for treating a plant or soil. The chemicals can be mixed, orpremixed, configured, and used as pesticides, herbicides, fungicides,insecticides, fungicides, adjuvants, growth enhancers, agents,artificial pollination, pheromones, etc., or a combination thereof. Inone example, water or vapor can be substituted for any of the fluid orchemical selections described above. In one example, the agriculturaltreatment system 400 agricultural treatment system 400 can apply powdersprays or projectiles as well as foams, gels, coatings, or otherphysical substances that can be emitted from a chemical spray device.

FIG. 5 illustrates a system 402 for selecting and producing a chemicalmixture for spraying. In one example, the system 402 can be a subsystemcombined with the agricultural treatment system 400 and mounted orattached to a vehicle. In one example, the system 402 can be implementedin real time such that an emitter 470 of the agricultural treatmentsystem 400 a gricultural treatment system 400 can receive instructionsto target and spray and a chemical selector 488 a can provide a desiredchemical mixture in real time. For example, multiple series of chemicalselection components can be configured such that each series of chemicalselection components can be run in parallel for a chemical mixer 488 ato mix chemicals, in the form of fluids, liquids, gas, powder, water,vapor, etc., in real time, and send the desired mixed chemical, in bothcontent, proportionality, concentration, and volume to the treatmentunit, or an emitter 470, to be emitted as a projectile, aerosol, mist,or a powder or liquid droplet onto a surface of an object. In oneexample, a first series of components for chemical selection can includea chemical tank 482 a, a chemical pump 485 a, a regulator 486 a, anaccumulator 487 a, and one or more spray tubes and potential circuitryto link each of the chemical tank 482 a, chemical pump 485 a, regulator486 a, and accumulator 487 a in series to be connected to the chemicalmixer 488 a. The chemical tank 482 a can store a desired chemical, whichcan be a premixed chemical another set of chemicals. For example, thechemical tank 482 a can store chemical-1. In parallel to the series ofchemical selection components of 482 a, 485 a, 486 a, and 487 a, is asecond series of chemical selection components including a chemical tank482 b, chemical pump 485 b, regulator 486 b, and accumulator 487 b. Thecomponents 482 b, 485 b, 486 b, and 487 b can be connected in serieswith one or more spray tubes and connected to the chemical mixer 488 a.The chemical tank 482 b can store chemical-2, which can be differentchemical mixture or concentration as that of chemical-1. In thisconfiguration the chemical mixer 488 a can select and extract, in realtime on the vehicle during an observation and spray trial, eitherchemical-1, chemical-2, or a combination of both with varyingconcentrations and volume. The chemical mixer 488 a can then send themixture of chemical-1 and chemical-2 or any desired mixture ofchemicals, or a chemical from only a single channel, to the emitter 470to emit a mixed chemical projectile, droplet, aerosol, etc., at a targetobject. Further, any number of different chemical mixtures can be storedon-board the vehicle such that the chemical mixer 488 a can extract thechemical mixture and generate a new chemical mixture for treating anobject. For example, a third series of chemical selection components,including a chemical tank 482 c, configured to store chemical-3,chemical pump 485 c, regulator 486 c, accumulator 487 c, can beconfigured in parallel with the other two series of chemical selectioncomponents such that the chemical selector can choose from any of thethree different chemicals of chemical-1, chemical-2, or chemical-3.Further, the number of chemical tanks stored is limited to only theamount that the vehicle with the agricultural treatment system 400 cansupport including an nth series of chemical selection components, suchas chemical tank 482 n, chemical pump 485 n, regulator 486 n, andaccumulator 487 n, linked in series by a spray tube and connected to thechemical mixer 488 a. The chemical mixer 488 a can be configured toselect and receive different combinations in volumes of chemical-1,chemical-2, chemical-3, and so forth, to be sent to the emitter 470 andemit a pressurized projectile, aerosol, mist, or a powder or liquiddroplet onto a surface of an object. In one example, one of the chemicaltanks can store water or vapor such that the selection of the chemicaltank with water is used to dilute a solution of mixed chemicals.

In one example, the emitter 470 can emit a projectile, liquid, gas,aerosol, spray, mist, fog, or other type of fluid droplet induced sprayto treat a plurality of different plants in real time. An agriculturalscene can include a row crop farm or orchard planted with differentcrops. In this example, each row of plants can include a different typeof plant to by cultivated and treated such that the emitter 470 cantreat one row with one type of treatment, such as a chemical mixture-1,mixed and sent to the emitter 470 by the chemical mixer 488 a, andanother row with another type of treatment to a different crop or plant,such as a chemical mixture-2. This can be done in one trial run by avehicle supporting the chemicals, and treatment system with emitter 470.In another example, each row itself, in a row crop farm or orchard, canhave a plurality of different type of crops. For example, a first rowcan include a first plant and a second plant, such that the first plantand second plant are planted in an alternating pattern of a first plant,a second plant, a first plant, a second plant, and so forth for theentire row of a first row. In this example, the chemical selector 488 aand emitter 470 can deposit a first chemical mixture projectile, forprecision treatment, to the first plant, and deposit a second chemicalmixture projectile, for precision treatment, to the second plant, inreal time, and back to the depositing the first chemical projectile tothe third plant in the row of crops, the third plant being of the sameplant type as the first plant, and so forth. In one example, a pluralityof more than two types or species of plants can be planted in tilledsoil, and be grown and treated in a row crop with the agriculturaltreatment system 400 with system 402.

In one example, the treatment unit of agricultural treatment system 400can blast water or air, or a water vapor to one or more agriculturalobjects to wash off any undesired objects detected on the surface orother portion of the agricultural objects. The undesired objects can beunwanted bugs or debris on the agricultural object as well as previouslyapplied chemicals that are no longer desired to leave on theagricultural object. In one example, the treatment unit can then recoatan agricultural object that was previously cleaned with water or airwith a new chemical treatment. In one example, one of the chemical tankscan also include water for the purposes of purging the stream of liquidfrom tanks to the treatment units of any excess chemical or substancebuildup which could affect chemical composition, pressure, spray health,and other controlled factors that could affect desired performance. Inone example, one of the tanks can include water for chemigation as wateris mixed with substance from a different tank.

FIG. 6 is a diagram 600 illustrating an example vehicle 610 supportingan example observation and treatment system, or treatment system 612,performing in a geographic boundary, according to some examples. In thisexample, the vehicle 610 can support one or more modular treatmentsystems 612. The treatment systems 612 can be similar to that ofagricultural observation and treatment systems described above. Forexample, a system can include onboard and offline components performingtasks both in real time while a vehicle supporting the onboard portionsof components are performing observations and actions and at edgecompute device or remotely both in real time or offline.

For example, the treatment system 612 can be one of a plurality ofmodular component treatment systems, each component treatment system caninclude one or more sensors including image capture sensors,illumination devices, one or more treatment units, for example a pair oftreatment units each with a treatment head capable of aiming at a target660 with at least 2 degrees of rotational freedom, a compute unitconfigured to send and receive instructions of sensors, encoders, andactuators and connected and associated with the component treatmentsystem and the compute unit to time sync all of the components, andother electronics to sync and communicate with other compute units ofother component treatment systems. Each of these treatment systems 612can receive treatment fluids from a common pressurized source of fluid,or each treatment unit is connected to different sources of fluid. Thecomponent treatment systems are configured to sense targets 660 in realtime while supported by the moving vehicle 612, determine what kind oftreatment, or other action, to perform on to a surface of the target660, target and track the target 660, predict performance metrics of theinstructed parameters of the action including projectile location,perform the action, including emitting a fluid projectile or lightsource, and evaluating the efficacy and accuracy of the action.

Additionally, each of these treatment systems 612, or componenttreatment systems, can communicate and receive information from acomponent navigation system or navigation unit which is configured tosense a global scene such that each of the compute units of each of thecomponent treatment systems can sense its local environment from sensorsoperably connected to the compute unit of the component treatmentsystem, or embedded in the component treatment system, as well asreceive information about the global environment in a geographic scenefrom sensors and analysis performed by sensors and the one or morecompute units of the navigation unit connect to each of the localcomponent treatment systems.

The vehicle 610 can be operating in a geographic region such as a farmor orchard. A portion of the geographic boundary, illustrated in FIG. 6, with one or more trees 634 is shown. In this example the vehicle 610can be operating in an orchard with multiple rows of trees, each havinga trunk 636, or other plants for the treatment systems 612 to observeand treat. In one example, the vehicle can be travelling in a straightline along a row of trees and crops on both sides of the vehicle.

One or more treatment systems 612 can be mounted on top, embedded in,suspended underneath, towed, or oriented in many ways securely onto thevehicle such that the treatment system 612 can be configured andoriented to scan a row of crops or plants or other agricultural scenesin a line while the vehicle 610 is moving.

The vehicle 610 may include functionalities and/or structures of anymotorized vehicle, including those powered by electric motors orinternal combustion engines. For example, vehicle 610 may includefunctionalities and/or structures of a truck, such as a pick-up truck(or any other truck), an all-terrain vehicle (“ATV”), a utility taskvehicle (“UTV”), or any multipurpose off-highway vehicle, including anyagricultural vehicle, including tractors or the like. The treatmentsystems 612 that may be powered or pulled separately by a vehicle, whichmay navigate paths autonomously in the geographic boundary.

In one example, a geographic boundary can be configured to have two rowsof plants on each side of a single lane for a vehicle to navigatethrough. On each side of the vehicle will be vertically growing plantssuch as trees. The treatment system 612 can be mounted on the vehicle ina way that image sensors of the treatment system 612 are pointingdirectly at the trees on each two left and right side of the vehicle. Asthe vehicle operates along a lane or path in the orchard, the treatmentsystem 612 can capture a series of images from one side to another ofthe row of plants as well as treat each agricultural object with aprecision treatment.

FIG. 7A illustrates a diagram 700 depicting an agricultural observationand treatment system supported by a vehicle navigating in anagricultural environment. The agricultural environment can be a farm ororchard typically having one or more plants such as trees 303. While theillustration depicts a system in an environment similar to that of anorchard, different, the description below can apply to a system, thesame system, performing in multiple different types of environments suchas row crop farms where portions of the system include sensors pointingat the ground to detect row crop objects of interest.

The agricultural observation and treatment system can be a modularsystem similar to that of agricultural observation and treatment system311 supported by vehicle 310. The system 311 can also include varioussensors such as imaged based sensor 313, lidar based sensors 314, or aplurality of non-vision-based sensors as described previously. Similarto that of navigation unis and navigation modules described in thisdisclosure, the treatment system depicted in diagram 700 can usesensors, such sensor 313 and 314 to perform global registry of theagricultural environment as well as perform localization and poseestimation of the vehicle or portions of the vehicle in a global sceneor from a global point of reference. This can include receiving sensordata and generating and building high definition 2-dimensional and3-dimensional maps, or global maps as opposed to views of individualcrops with sensors close up to the individual crops which can bereferred to a local scene or local registry of a geographic boundary orscene, of the agricultural environment in real time.

In one example, the agricultural observation and treatment system 311,described in previous discussions, can be configured to perform sceneunderstanding, mapping, and navigation related analysis includinglocalization of the vehicle and/or components of the treatment systemand pose estimation of the vehicle and/or components of the treatmentsystem, for example pose estimation of individual image capture devicesembedded in each component spray system, each component spray systemillustrated in diagrams 900, 902, 2406, and 2408, or each modularsubsystem of the agricultural observation and treatment. The sensing canbe performed with a various suite of image-based sensors, motion-basedsensors, navigation-based sensors, encoder sensors, other sensors, or acombination thereof, fused and synchronized together such that at leastcomponents of the agricultural observation and treatment system 311 candetermine navigational properties of an environment the system is in,including pose and pose estimation of the components in real time as thevehicle, treatment system, and components of the system moves andnavigates in such environment.

In one example, the agricultural observation and treatment system 311can perform mapping of a scene and localizing the treatment system inthe scene. This can include mapping a scene with a global frame ofreference or point of origin in a given coordinate system, anddetermining its location relative to a point in the mapped scene,particularly a point of origin in the scene. For example, when a vehicleis navigating in an agricultural environment in FIG. 7A, the system canarbitrarily determine a point of origin in the agricultural environment,for example the portion of the agricultural environment the system hassensed thus far, or preloaded into the system before sensing theenvironment in real time. For example, a first corner or first edge of aportion or region of a farm or orchard. As the vehicle navigates in theenvironment, the system can determine where the vehicle and each of thecomponents of the system, due to the components being fixed relative tothe vehicle, has moved across time. The treatment system's navigationtype sensors, including GPS, IMU, encoders, image capture devicesconfigured to capture a high resolution or low resolution of a globalscene and perform techniques and functions in computer vision andmachine learning such as visual SLAM and visual odometry (the globalscene referring to the farm or orchard, or any kind of agriculturalenvironment itself, and not necessarily those sensors pointing directlyat individual plants or crops of plants for high-definition images ofindividual plants), Lidars to sense a global scene similar to that ofthe image capture devices, optoelectrical sensors, ultrasonic sensors,radar, sonar, and other image capture devices such as NIR cameras, RGB-Dcameras, multispectral cameras, etc., configured to obtain globalregistry of an environment including mapping the environment at a globallevel, and can be used to generate and continuously generate a globalpose estimation of the vehicle as it moves along a path, and each of itscomponent, relative to a point of origin in the geographic environment,the system can also determine the same global pose of components of thesystem as well, due to the components being rigidly connected orsupported by the vehicle. For example, while a camera sensor that is 15feet from the ground or 15 feet vertically above a bed of the vehiclemay wobble and move more than that of a camera sensor that is 1 footfrom the ground or 1 feet vertically above the bed of the vehicle, eachposes of the cameras may be different to each other at a local level,relative to a vehicle, and therefore, relative to a point of origin inthe geographic scene, the global pose estimation can be estimated tothat of the global pose estimation generated for the vehicle by anavigation module and sensors associated with navigation. This isbecause each of these cameras can be rigidly connected to the sensorsconfigured to perform global registry of the environment, such thatphysical translation and movement of the vehicle, and particularlymovement of the navigation-based sensors (for example, GPS, andnavigation-based camera sensors), the local sensors embedded orsupported by each component spray module or component treatment modulewill substantially move the same amount. Additionally, each sensorsupported by each component treatment module can track local objects,shapes, patterns, or any salient points or patterns local to each of thecomponent treatment modules such that a more accurate pose estimationfor each of the component treatment modules, more specifically, poseestimation of sensors of each component treatment modules, can begenerated and continuously generated as the component treatment modulesare scanning a local scene to observe objects and perform objects ontarget objects.

In one example, the system, both the navigation system and itscomponents, sensors, and compute units, as well as each componentsubsystem or component treatment module having its own components,sensors, treatment units, and compute units, can use techniquesassociate with simultaneous localization and mapping (SLAM) andodometry, particularly visual SLAM, VSLAM, and visual odometry, or VIO,in conjunction with other non-visual based navigation and localizationanalysis, fused together in real time with sensor fusion andsynchronization, to perform pose estimation of the vehicle.Additionally, each modular sub systems of the treatment system includingeach modular spray subsystem, for example each modular spray subsystemor component treatment module including a structural mechanism, acompute unit, one or more sensors, one or more treatment units, and oneor more illumination devices, can perform VSLAM and receive othernon-visual based sensor readings, and continuously generate its ownlocalized pose estimation, the pose being relative to specific objectsdetected by each of the component treatment modules, which can includeagricultural objects including target objects or nearby objects orpatterns, shapes, points, or a combination thereof that are of a similarsize to that of the target objects. The pose estimation of components ofeach of the component treatment modules will be relative to the locationof the objects and patterns detected to be tracked across time andacross sensors in stereo for stereo matching points for depthperception. Additionally, the system can perform projection andreprojection, and determine reprojection error, for more accuratelydetermining location of objects and eliminating outliers. Thus,detecting objects and patterns that are known to be fixed in space, forexample a ground terrain with unique rocks or dirt patterns, orindividual plants, and calculating and identifying the objects' orpatterns' 3D location and/or orientation relative to the sensors' 3Dlocation and/or orientation sensing the objects and patterns allows thesystem to understand navigation, localization, and more specificallylocal pose estimation of each of those sensors relative to the objectsdetected. Additionally, since the orientation of the treatment units,and its treatment heads, are in close proximity to the individualcomponent treatment module, and rigidly attached and connected to astructure of the component treatment module (illustrated in at leastFIGS. 9A, 9B, and 24E), and also in close proximity and rigidlyconnected to the sensors associated with that particular componenttreatment module and compute unit, the location and orientation of thetreatment head of the treatment units (the treatment heads havingencoders to determine line of sight relative to the body of thetreatment unit) can also be continuously generated and determinedrelative to the target objects or objects near the target objectsthemselves for better accuracy of treatment.

In this disclosure, while the determined pose estimation can be referredto the pose estimation generated for the vehicle or a component modularspray subsystem, a pose estimation can be determined, using VSLAM, VIO,and/or other sensor analysis, to generate a pose, including a locationand/or orientation for any component of the vehicle or component of theagricultural observation and treatment system. In one example, a poseestimation can be referred to and generated with coordinates, forexample (x₁, y₁, z₁, Φ₁, θ₁, Ψ₁) with x, y, z, being the translationallocation relative to an origin (x₀, y₀, z₀) and starting orientation(Φ₀, θ₀, Ψ₀) of the component relative to an origin point and/ororientation, of any component or portion of a component.

In one example, as illustrated in FIG. 7A, a vehicle's navigation modulecan include sensors such as imaged based sensor 313, lidar based sensors314, or a plurality of non-vision-based sensors as described previously,fused together to obtain global registry of the scene for mapping thescene as well as in real time navigating in the scene. For example, apair or more than one pair of image sensors 313, in stereo, can bemounted on the vehicle such that the sensors are pointed out to the realworld to observe a global scene, as opposed to down on the ground toobserve individual plants or right in front of (a few meters or feweraway) a tree to observe individual plants, crops, or other agriculturalobjects. Additionally, Lidars, radar, sonar, and other sensors can bemounted in a similar location as that of the cameras to register aglobal scene.

The agricultural observation and treatment system, at the navigationunit, or a component of the navigation unit, can perform real time VSLAMand VIO to simultaneously map the agricultural environment that it isin, as well as understand the location, localizing, of the agriculturalobservation and treatment system itself as it is navigating in theenvironment, whether it is autonomously navigating or navigating with ahuman driver on the vehicle or remotely. In one example, as illustratedin diagram 700 of FIG. 7A, VSLAM can be performed using keypointdetection and matching across stereo and across time, or surfacematching of salient points or regions of surfaces detected. Keypoints,for example keypoint 706 can be generated in real time from image framescaptured by image sensors. In one example, stereo image sensors cancapture frames at the same time. Keypoints can be generated andidentified by a compute unit embedded in the image sensor, or a computeunit of the navigation unit operably connected to each of the stereoimage sensors configured to receive images or imagery data from thesensors. Common keypoints are matched such that the system canunderstand depth of the keypoint from the stereo sensors, and thus thedepth of the keypoint from the navigation unit and therefore thevehicle. Thousands of keypoints can be detected, generated, and trackedover time per frame. They keypoints themselves can be cluster of pixelpoints representing a corner, blob, line, other salient patterns. Thesepoints do not necessarily have to be real world identifiable knownobjects. For example, a keypoint can be generated as a where two objectsmeet, or where one object and a background meet, for example an edge ofa leaf from the background sky. The different in color between a leafand a sky will create a line or edge between the two colors captured byan image sensor. Other examples can be corners of objects, dots, orlines. For example, three small rocks next to each other can formsalient pattern to track, even if the system doesn't understand that itis a 3-rock cluster, meaning the system cannot extract enough featuresto perform object detection and determine that one or more rocks wereidentified. But the actual 3 rocks form a rigid and complex pattern thatthe system can still identify and track.

The system may not be able to detect an identity of an object, but itcan detect its contours and edges and track those points. In thisexample any type of points or pixels, clusters of points or pixels,lines, corners, that the system may determine as salient points orpatters, can be generated as a keypoint. The keyponit may or may not begenerated in a different frame captured by a different image sensor, forexample a second sensor in stereo with a first sensor. Common keypointsbetween different frames can be matched using various computer visiontechniques such as image correspondence, keypoint matching, templatematching where the templates are patches of image including keypoints,dense SLAM techniques, techniques with classifiers, RANSAC and outlierrejection to more accurately detect common keypoints, other statisticalmodelling techniques, or other corner, line, blob, edge detectiontechniques including FAST, SIFT, Harris corner detection, Lucas-Kanadetracking, SURF, NERF, ORB, or a combination thereof. Additionally,lines, corners, patterns, or other shapes, whether referred to askeypoints or not, that are generated in each frame can be matched to thesimilar lines, corners, patterns, and shapes using the same or similartechniques described above.

The keypoints that are matched between two cameras in stereo, or morecameras can be used to determine depth of the object or point or pointsin the real-world space associated with the keypoints detected. Thesetechniques can be applied similarly with Lidar or used in conjunctionwith lidar sensed point clouds and fused together for more accuraterepresentations of a scene. The images sense, and keypoints matchedbetween cameras can be used to perform dense reconstructions ofenvironments, as well as perform real time navigation, localization, andpose estimation of the sensors sensing the environment.

In one example, dense visual slam can be performed, whether by a singleimage sensor, such as a camera, infrared camera, rgb-d camera,multispectral sensors, or other sensor, or by stereo cameras, or two ormore of different types of cameras that are synchronized with knownfixed distance and orientation relative to each other, to compare imagefrom frame to frame across time. The commonly matched and tracked pointscan allow the sensor to determine how much movement and amount oftranslation and orientation change of the sensor based on the shape anddepth of the point or object detected and tracked from a previous frame.For example, an agricultural observation and treatment system can sensea point in space, which can be any type of pattern, but for example, canbe a pattern of a base of a tree trunk. The system can determine that asa keypoint 706. As the vehicle supporting the agricultural observationand treatment system moves closer to the base of the tree trunk. Thepattern of the keypoint 706 from a first frame will change in shape,location, size, etc., in a subsequent frame captured at a later point asthe image sensor has moved closer to the point. The system would be ableto calculate the amount of movement in space as it tracks the same pointin space with keypoint matching or other techniques to perform VSLAM.

In one example, VSLAM can be performed by detecting objects themselves,rather than arbitrary points and patterns detected in an image frame.For example, a whether referred to as keypoints or not, the system caningest a frame, perform feature extraction and object detection anddetect specific known objects with a machine learning model. Theseobjects, rather than salient points such as corners, lines, blobs, orother patterns that can be tracked across frames in stereo and acrossframes in time, can be objects such as agricultural objects andlandmarks, such as leaves, weeds, rocks, terrain, trees, components ofirrigation systems, wheels of a vehicle, or any other landmarks wherethe system can identify the landmark itself, rather than just salientpoints that may or may not be associated to a landmark. For example,referring to diagram 1300 a of FIG. 13 a , the agricultural observationand treatment system can detect a plurality of fruitlets, buds, andlandmarks in a single frame using a machine learning detector embeddedon board the system. From frame to frame, as the treatment system scansthe orchard, the image sensors of each of the component treatmentmodules configured to detect individual objects and landmarksthemselves, can detect objects, and match the object detections fromframe to frame for the purposes of SLAM and pose estimation of thesensor sensing the object, in addition to determining whether to trackan object to perform a treatment action. In another example, asillustrated in diagram 1600 of FIG. 16 , the component treatment modulesensing and analyzing the frame or image 1610 can detect everyagricultural object in the frame including carrots (or agriculturalobject not to treat) and weeds (agricultural objects detected fortreatment). The system can identify features of each object, and matchthe features from frame to frame for scene understanding and determiningmovement and orientation of the sensors capturing the frame (because theobjects themselves are not moving in space) relative to those objects,even though the agricultural object and treatment system under certainconfigurations are tasked to only target, track, and treat the weeds.The objects can be detected using feature extraction and objectdetection with various machine learning techniques, computer visiontechniques, or a combination thereof. Additionally, while the discussionabove focuses on image-based sensors, similar techniques can be appliedusing one or more lidar sensors for point cloud to point cloud matching.

In one example, in additional to performing functions to allow a systemto determine pose and therefore navigate in an environment, the systemcan use the same sensor readings used to determine pose estimation toperform dense reconstruction of a scene and map the agriculturalenvironment. This can be done with VSLAM which takes multiple viewsacross frames in time and in stereo to reconstruct a scene. Othertechniques can include dense reconstruction of a scene from SLAM orstructure from motion. Other techniques for improvements in scenegeometry in a sequence of frames captured by an image sensor can includelocal bundle adjustment to improve visual SLAM.

In one example, the agricultural objects discussed in this disclosurecan be any number of objects and features detected by various sensorsincluding image sensors. The agricultural objects can include varietiesof plants, different phenological stages of different varieties ofplants, even though the specific object detected in a geographic scene,having different physical features due to its growth is the samephysical object in the geographic scene, target plants to treatincluding treating plants to turn into a crop or treating plats forplant removal or stunting or stopping or controlling the growth rate ofa plant. Agricultural objects can include soil or patches of soil forsoil treatment. Other objects detected and observed by a treatmentsystem can include landmarks in the scene. Landmarks can be trees andportions of trees including spurs, stems, shoots, laterals, specificportions of the terrain including dirt, soil, water, mud, etc.,trellises, wires, and other farming materials used for agriculture.Additionally, landmarks can be any object that can be detected by theobservation and treatment system and tracked as a vehicle supporting thetreatment system is moving as well as tracked throughout time as thevehicle visits the location across a grow season in multiple trials orruns. For example, agricultural objects described throughout thisdisclosure can also be considered a landmark for tracking the landmarkeven though the observation and treatment system may not necessarily betargeting the object for treatment or tagging and indexing the objectfor observation. The landmarks can be tracked, using SLAM or othercomputer vision and machine learning based or assisted computer visiontechniques, to better locate a nearby landmark or object that will be atarget for treatment. For example, a plurality of landmarks andpotential landmarks that are also target objects can be detected in agiven image frame, or a pair of stereo frames, or sensed by a pluralityof sensors synched in time. Once the agricultural observation andtreatment system has identified

In one example, landmarks that can be tracked are not specificallydefined objects in the real world, but patterns or combinations ofobjects or features such as region in space separating one or moreunknown objects from a background. While the system may not be able toperform feature extraction enough to detect an object's identity, it canstill detect that an object, or a pattern created by one or moreobjects, exists in the captured sensor reading, and is that of one thatis fixed in space and will not move. These detections can also bereferred to as landmarks and used to track the landmarks for real timepose estimation. For example, landmarks detected as illustrated indiagrams 1300 a and 1300 b of FIGS. 13A and 13B can be used to generatekeyframes for further offline analysis including matching frames takenat different times of the same or similar view to create a time lapsevisualization of an object by comparing only keyframes instead of everyframe ingested by a sensor, determining which candidate keyframepreviously generated and stored by the system is used in real time toperform image correspondence for live/real time detections, targeting,and tracking, as well as used and tracked from frame to frame to performVSLAM, and generate better pose estimation for the sensor sensing theframes.

An agricultural object of interest can be a target plant for growinginto a harvestable crop. In one example, the agricultural object ofinterest can be a target plant to remove, such as that of a weed, or anyplant that is not a crop. In one example, the agricultural object can beportions of a soil of interest to observe and cultivate, such that atleast a portion of the cultivating process is treating the soil with oneor more fluid chemical treatments or fertilizer.

In one specific example, the agricultural observation and treatmentsystem can perform a variation of SLAM focusing on one or more specificfeatures to extract to more accurately generate pose estimation foragricultural observation and treatment systems performing inagricultural environments. For example, a system can be embedded with aSLAM algorithm, whether assisted by machine learning, other computervision techniques, or both, to detect tree trunks. In most orchards,tree trunks do not grow in size, change shape, or move in relativelyshort spans of time. Tree trunks are also spaced apart enough that eachtree trunk detected can allow a system to determine a different set ofagricultural objects detected in a tree are separated, which can be usedto determine which sets of frames in a plurality of frames can begenerated or used as a keyframe.

In another specific example, the agricultural observation and treatmentsystem can perform a variation of SLAM focusing on one or more specificfeatures to extract to more accurately generate pose estimation foragricultural observation and treatment systems performing inagricultural environments. For example, a system can be embedded with aSLAM algorithm, whether assisted by machine learning, other computervision techniques, or both, to detect beds, troughs, furrows, andvehicle tracks of a row crop farm, the beds being where plants areplanted and grow, and tracks being where vehicle wheels can travel.Instead of detecting arbitrary lines in a given frame, a machinelearning assisted SLAM algorithm can be configured to detect just thebeds and troughs, due to the beds and troughs looking substantially likelines that would take up an entire frame. This can help ease theperformance load on performing VSLAM as the vehicle only needs tooperate between two lines, detected as a trough, to better minimizedrift.

In one example, each of the compute units of each component treatmentmodules can associate local and global pose. While the compute unit andsensors associated with the navigation unit performs slam and accountsfor a vehicle pose (the vehicles components' own SLAM), for navigationpurposes, it can also combine to individually map every single plant,because the plant will have a known location due to its relativelocation to the component treatment modules' sensors, for example stereoimage sensors. The stereo image sensors will know where it is relativeto a global scene to do the mapping of a local scene and the real timelocalization of the vehicle itself in the global scene. The combinationwill allow the system to generate a global map with every single uniqueand individual agricultural object sensed, and indexed with highprecision in the global map itself.

In one example, a drone with one or more image sensors, lidars, GPS,IMU, can be configured to scan and map a scene of a geographic boundaryof an agricultural scene and combine the sensor readings of the globallymapped scene from the drone with that of the globally mapped scene fromthe navigation unit's sensors and compute units of the agriculturalobservation and treatment system and the locally mapped individualagricultural objects and landmarks imposed onto the globally mappedscene generated by the agricultural observation and treatment system.This would allow any views or scenes not necessarily or accuratelycaptured by the agricultural observation and treatment system on theground but could be captured by the done to be accounted for to createan even more accurate global map with more views of the map or indexeddatabase of a global map comprising a global scene from the drone withhigh definition readings of the geographic boundaries and views of theboundaries, a denser global scene of portions of the agriculturalenvironment including trees, rows, beds, troughs, and it's locationrelative to each other and in the global frame, as well as a local sceneof each individual agricultural object and landmark detected by eachcomponent treatment module.

In this example, individual crops, plants, agricultural objects, andlandmarks can be sensed and registered with location and pose relativeto image sensors sensing the individual crops, plants, agriculturalobjects, and landmarks. This will allow the agricultural observation andtreatment system, at each of the component treatment systems havingsensors and treatment units to identify more accurately where eachobject is relative to the local sensors of each component treatmentmodule in real time. Additionally, the location of the individual crops,plants, agricultural objects, and landmarks, relative to the localsensors of each component treatment module can be indexed and stored.Because the local sensors of each component treatment module are rigidlyconnected to each other with a support structure, and the supportstructure is also rigidly connected to the vehicle supporting thenavigation module and its sensors for obtaining global registry andglobal pose, The agricultural observation and treatment system cancombine the local and global pose to determine where each individualcrops, plants, agricultural objects, and landmarks sensed is located ina global scene. Thus, the agricultural observation and treatment systemcan be configured to create a global map of a scene with each individualobject and/or landmark detected in the global map with sub-centimeteraccuracy of where each individual object and/or landmark is located inthe global map, or at least digitizing and indexing an agriculturalscene without generating a visualizable map.

In one example, a vehicle or global pose estimation can be determined aswell as an individual localized pose estimation for each componenttreatment module can be determined.

For example, each treatment module, having one or more image sensors,one or more illumination sources, and one or more treatment units (beinga spray device or a light treatment device) and a compute unit alloperable and rigidly attached to each other as a single modulartreatment module, for example shown in diagram 900 of FIG. 9 , each ofthe individual treatment modules can compute its own pose estimation.This in effect allows for each treatment unit to have a more accuratepose estimation, for example, a local pose estimation of the treatmentunit and the image sensors of the same treatment module supporting thetreatment units of the treatment module locally to each other and to theground row plants or orchard trees that each of the treatment module'ssensors are sensing. Additionally, the sensors of the navigation unit ornavigation module, for example the navigation unit including a GPSsensor, one or more IMU's, one or more encoders, one or more cameras,for example facing outward into the geographic area as a whole eitherfacing forward or backwards of the direction of the vehicle path, or oneor more laser or lidar sensors, can be configured to generate a globalpose estimation for the vehicle itself. In this example, each treatmentunit of each treatment module can rely on both the pose estimationdetermined by the treatment module's local sensors and compute units,for the local pose estimation of each treatment module, as well as thepose estimation determined by the navigation unit's sensors and computeunit, for the global pose estimation of the vehicle. The combined andfused pose estimation can be configured to give each treatment module amore accurate localization, orientation, and pose such that as eachtreatment module detects, targets, and tracks each object of interestdetected, the treatment unit can target and treat the agriculturalobjects of interest more accurately.

FIG. 7B illustrates an example method 702 that may be performed by someexample systems, subsystems, or components of systems, described in thisdisclosure either online, that is onboard a vehicle supporting one ormore modular agricultural observation and treatment systems, subsystems,or components of systems, or offline, that is at one or more servers oredge compute devices.

For example, at step 710, an agricultural observation and treatmentsystem can initialize. At step 720, the agricultural observation andtreatment system can determine a real-world geospatial location of thetreatment system. The determining of location can be performed bylocation-based sensors such as GPS, image-based sensors, such as camerasincluding CCD, CMOS, or Lighfield cameras, multispectral cameras, RGB-Dcameras, NIR cameras, the same two cameras in stereo or variations ofcameras synched in stereo, or more than two synchronized together,Lidar, laser sensors, motion sensors such as IMU, MEMS, NEMS, ormotion-based encoders. At step 730, the agricultural observation andtreatment system can receive one or more images or point clouds from oneor more image capture devices. At step 740, the agricultural observationand treatment system can identify one or more salient points or salientregions of at least a portion of a first frame. The salient points canbe keypoints and the salient regions can be keypoint regions, cluster ofpixels in a frame that is associated with a fixed object or points inspace that can be compared for similarities, including keypointmatching, across stereo sensors or across time by frames captured by thesame sensor from frame to frame. The salient points themselves can beobject based, instead of keypoint based, such as objects that can bedetected with a neural network using feature extraction and objectdetection. In one example, the salient points can be points or regionsdetected by a machine learning detector, keypoints generated usingvarious computer vision algorithms or by a machine learning detector ora machine learning assisted computer vision algorithm, or a combinationthereof. At step 750, the agricultural observation and treatment systemcan identify one or more salient points or salient regions of at least aportion of a subsequent frame. At step 760, the agricultural observationand treatment system can determine a change in position of the treatmentsystem based on comparing the first and subsequent frame. At step 770,the agricultural observation and treatment system can verify or improvethe determined change in position with the position determined by thelocation-based sensors, motions sensors, or both. At step 780, theagricultural observation and treatment system can determine a poseestimation. And at step 790, the agricultural observation and treatmentsystem can send instructions to activate actuators. The points ofinterest to track for motion estimation and SLAM can be that ofreal-world objects or patterns detected, or salient cluster of points inan image or point cloud that can be tracked from frame to frame or pointcloud to point cloud as a vehicle with image or point cloud sensors movein time. These can be detected by computer vision methods of detectingedges, corners, blobs, lines, etc., or by a machine learning algorithmconfigured to detect real world objects, such as agricultural objects,for example leaves for sensing systems pointed down at row crops, rocks,dirt, beds, troughs, crops, weeds, etc. For example, if a landmark suchas a small rock in the dirt, or a leaf of a crop, in a frame captured byan image sensor, the compute unit can determine that a cluster of pixelsof the frame comprise a “rock” detected by a machine learning algorithm,such that an object that is the rock detected can be tracked and matchedacross stereo vision system and across time, that is from frame toframe, and used to perform motion estimation and pose estimation bytracking relative position of the suite of fixed sensors, and byextension the position of the vehicle, the agricultural observation andtreatment system supported by the vehicle and each of its treatmentmodules, to the rock as the vehicle moves in a direction relative to therock, or any object detected by the machine learning algorithm.Alternatively, the compute unit can detect that a cluster of pixels inthe frame captured is different enough from a detected background of theframe such that the cluster of pixels, while the compute unit may notknow, extract enough features to determine and detect, that it is areal-world rock, is still a real-world object that is stationary and canbe tracked from frame to frame, and by extension, can be compared tofrom frame to frame, to perform motion estimation and pose estimation.In one example, the objects detected in real time can be used todetermine which areas detected in a geographic scene should be treated.Additionally, the objects detected in real time can also be used todetermine motion estimation and pose estimation of the sensor sensingthe objects themselves, and by extension the pose of the vehicle andagricultural observation and treatment system and each subsystem, forexample each treatment module, on board the vehicle.

FIG. 7C and FIG. 7D illustrate additional example methods of 702 thatmay be performed by some example systems, subsystems, or components ofsystems, described in this disclosure.

Alternatively, or additionally, at step 752, the agriculturalobservation and treatment system can compare one or more salient pointsor regions of the first frame with the one or more salient points orregions of one or more subsequent frames. This can be done with a cameraby comparing frame to frame, can be done with two or more cameras bycomparing left and right, or top and bottom frame for depth, or fromframe to frame from a first camera to the next or from the first camerato the second camera, or a combination thereof. This can also be donewith different types of cameras, including comparing and matchingsalient points of an image from a visible color image with that of aninfrared image and/or with that of an ultraviolet image and gettingstructure or the object or salient points, location, and pose from thecomparison, not necessarily from motion of from frame to frame in timefrom the same sensor, but from frame 1 of camera 1, frame 1 of camera 2and frame 1 of camera 3 at the same time. At step 754, the agriculturalobservation and treatment system can generate one or more 3D models ofobjects identified in at least one or the first or subsequent frames,associated with objects in the real world. In one example, the objectcan be target objects of interest, objects related to sprays such ascapturing the spray projectile itself, splash health, and splatdetection, referring to whether a spray projectile hit the target bymeasuring the splat size and location of the projecting creating a“splat” on the object and/or surface of the ground near the object.

Alternatively, or additionally, at step 756, the agriculturalobservation and treatment system can generate one or more global maps ofa real-world geographic area including objects identified in at leastone of the first or subsequent frames, or both, associated with objects,landmarks, or both in the real world.

In one example, tracking to find the same feature from a first framedetected to subsequent frames, for example using Lucas-Kanade tracking,under the assumption that the object does not move far away from frameto frame, for tracking an object on a moving vehicle. The featuredetected in the first frame for tracking can be real world objectsdetected and identified by one or more machine learning algorithms onboard, or by real time edge compute, the treatment modules of thetreatment system performing observation and actions in real time online,objects that can be represented by a cluster of salient points in aframe, such as corners, lines, blobs, edges of an object, detected bycomputer vision techniques such as FAST, SIFT, SERF, or other techniquesdescribed in this disclosure.

In one example, the objects detected in real time can be used todetermine which areas detected in a geographic scene should be treated.Additionally, the objects detected in real time can also be used todetermine motion estimation and pose estimation of the sensor sensingthe objects themselves, and by extension the pose of the vehicle andagricultural observation and treatment system and each subsystem, forexample each treatment module, on board the vehicle.

FIG. 8 illustrates an example schematic block diagram of componentrythat may be utilized with a system 800 similar to that of agriculturalobservation and treatment systems described previously in thisdisclosure. The system 800 may include a sub-system 802 thatcommunicates with one or more perches, or treatment modules 804. Thetreatment module 804 can be a component of a modular system of one ormore treatment devices. In each treatment module 804, the treatmentmodule 804 can include, one or more image sensors 820 and 822, and oneor more illumination units 824. In one example, an agriculturalobservation and treatment system, described in this disclosure, can bereferred to as a portion of a system for observing and treating objectsthat is onboard a moving vehicle. Performances by the portion of thesystem onboard the moving vehicle, including computations, and physicalactions, can be considered online performance or live performance. Aportion of the system comprising one or more compute or storagecomponents, that are connected as a distributed system, can beconsidered the offline portion of the system configured to performremote computing, serve as a user interface, or storage. In one example,the agricultural observation and treatment system is a distributedsystem, distributed via cloud computing, fog computing, edge computing,or a combination thereof, or more than one subsystem is performingcomputations and actions live in addition to the portion of the systemonboard a moving vehicle. In one example, treatment modules, a pluralityof treatment modules, or a first, second, etc. treatment modulesdiscussed in this disclosure can be treatment module 804 of diagram 800.The treatment module can be a subsystem having a compute unit, sensors,including image capture sensors, illumination devices, and one or moretreatment units comprised of one or more nozzles guided by a gimbal orturret mechanism, local to each treatment module. The totality ofmultiple modular treatment modules described in this disclosure,including treatment module 804 of diagram 800, can be part of a greateronline or on-board agricultural observation and treatment system havinga global compute unit and sensors sensing a greater geographicagricultural environment and communicating, sending, and accessinginformation and instructions to each modular treatment modulesubsystems. And the on board agricultural observation and treatmentsystem, supported by a vehicle and/or one or more edge compute devicesto perform computing functions, can be a subsystem or a node of agreater agricultural observation and treatment system having a meshnetwork of onboard observation and treatment systems across a fleet ofvehicles operating in multiple geographic areas, for example at multipledifferent farms and orchards having different crops requiring differentobservation and treatment services, and backend servers, compute andcloud compute subsystems configured to perform offline functions such asingesting performance logs and sensor data captured at each farm,perform analysis and quality analysis, perform training on one or moremachine learning models that can be uploaded to each on site or on boardsystem, and many other nodes including user interface for a user toengage in the functionalities discussed above.

The treatment module 804 can include a compute unit 806, which caninclude a CPU or system on chip, that sends data and instructions to anECU 818, or daughterboard ECU, for synchronization of operation of oneor more illumination units 824 and operation of image sensors 820 and822. The ECU 818 can sends/receives data to one or more cameras of imagesensors 820, and/or one or more cameras of image sensors 822, and one ormore illumination units 824 each including a light bar of LEDs,including instructions by the ECU 828 to activate the image sensors 820and 822 and illumination units 824.

The system 800 can also include a navigation unit 802 configured tointerface with each treatment module 804. The navigation unit 802 caninclude one or more components and modules configured to receivepositional, velocity, acceleration, GPS, pose, orientation, andlocalization and mapping data. In one example, the navigation unit 802can include a vehicle odometry module 808 with encoders and imagesensors to perform wheel odometry or visual odometry and process imagesand vehicle movement to calculate and determine a position andorientation of the vehicle supporting the system 800. The navigationunit can also include an IMU module 810 with one or more IMU sensors,including accelerometers, gyroscopes, magnetometers, compasses, and MEMand NEM sensors to determine IMU data. The navigation unit 802 can alsoinclude an GPS module 811 to receive GPS location data, for example upto a centimeter accuracy. The navigation unit can also include a SLAMmodule 812 for performing a simultaneous localization and mappingalgorithm and application for mapping an environment including anagricultural geographic boundary such as a farm, orchard, or greenhouse,and determining localization and orientation of a vehicle supporting thesystem 800, components of the system 800 relative to the geographicboundary, as well as localization and orientation of agriculturalobjects and scenes detected by the system 800. The SLAM module 812 cantake sensor data from one or more cameras, including stereo visioncameras, cameras that are omnidirectional, cameras that are movingrelative to the vehicle, or other sensors 813 including LiDAR sensors.The LiDAR sensors can be flash LiDAR sensors or static LiDAR sensors,spinning LiDAR sensors, other rangefinders, and other sensors discussedabove. As the navigation 802 receives sensing data related tolocalization and mapping, a compute unit 806, including a CPU or systemon chip, of the navigation unit 802 can fuse the sensing signals andsend the data to each of the treatment modules 804 or to a remotecompute unit or server through a communications module 840. The sensingcomponents of the navigation unit 802 can be activated and controlled byan ECU 814. The ECU 814 can also be configured to interface, includingactivation and power regulation, with each of the treatment modules 804.

The treatment module 804 can also include a treatment unit 828configured to receive instructions from the compute unit and ecu 818including treatment parameters and treatment trajectory of any fluidprojectile that is to be emitted from the treatment unit 828. The CPU602 to communications components to send and receive instructions toother components of the system 600 as well as remote devices. A chemicalselection unit 826 can include one or more chemical pump(s) configuredto receive non-pressurized liquid from one or more chemical tanks 832and operable to each treatment units of each of the treatment modules804, or multiple treatment units 828 of each treatment module 804. Oneor more chemical tanks 832 may have different types of chemicals. Thechemical pumps can send stored liquid or gas from the one or morechemical tank(s) 832 to one or more regulators 834, which will furthersend pressurized liquid to one or more other components in series as thepressurized liquid reaches the one or more treatment units 828 of system800. Other components in the series of the chemical selection unit 826can include an accumulator and chemical mixer 836 (described in previoussections of the disclosure). The treatment unit may emit the liquid at aparticular trajectory in order for the fluid projectile to come intocontact with an object and at a particular physical location.

In one example, as a vehicle performs a trial on a geographic boundary,each of the treatment modules 804 can perform actions independently ofeach other. Each treatment module 804 can receive its own imageacquisition and processing of images for treatment. The treatmentparameters can be determined locally on each treatment module 804,including object detection and classification of agricultural objects ina scene as well as determining treatment parameters based on the objectsand features detected. The processing and be performed by each computeunit 806 of each treatment module 804. Each of the treatment modules 804can receive the same data sensed, fused, and processed by navigation,vehicle orientation and position data from the navigation unit 802 sinceeach of the treatment modules 804 will be supported by the same vehicle.In one example, each of the treatment modules 804 can share the samechemical selection component 826. In one example, multiple chemicalselection units 826 can be configured to connect and interface with eachtreatment module 804 where one treatment module 804 can be configuredwith one chemical selection unit 826.

FIG. 9A illustrates an example modular treatment module 900, or perch.In one example, the modular treatment module 900 may be configured withmultiple illumination units 910 mounted to a frame 902, 903 orsupporting structure. The modular treatment module according to variousexamples may include multiple illumination units 910 of LED lights.Illumination unit 910 may include one or multiple LED lights includingan array of LED lights. The LED lights can each be packaged in anenclosure for better mounting of the LED lights to the rest of themodular treatment module. For example, a light enclosure can support 4individual LED Lights, each LED light can include a plurality of LEDdiodes to illuminate light. In another example, the LED Lights can bestandalone, supported by a structure or heatsink and individuallymounted to the rest of the treatment module. In one example, each LEDlight, having a plurality of LED diodes, can include one or more lensesto focus the illumination intensity, direction, or illumination area.The modular treatment module 900 may include a camera enclosure, orcamera bank 904 that includes one or more cameras or other image sensingdevices. In one example, the illumination units 910, treatment units1100, supported by treatment unit frame 903, can all be operably mountedand connected to the camera bank 904 having a camera enclosure. Theinner two cameras may be identification cameras to obtain digitalimagery of agricultural objects, and the outer two cameras may becameras used to obtain imagery of agricultural objects being treatedincluding the treatment projectile, treatment profile, splat detection,treatment health and accuracy. In one example, a pair of stereo camerascan be configured to ingest frames at a high frame rate and at a highexposure rate or refresh rate shutter speed. For example, the camerascan ingest images up to 8K definition per frame at 2040 Frames capturedper second. In this example, the compute unit embedded and enclosed inthe camera bank 904, configured to send instructions and read inputsfrom each of the treatment units 1110, sensors including cameras,illumination units 910, and so forth, action as the main compute unit ofthe component modular treatment module 900, can receive differentdownsamples of image quality and number of frames per second. Forexample, one or more FPGA's, ASICs, or one or more microcontrollers canbe embedded at the each of the camera modules, such that the camera'sexposure and shutter speed receives 8K definition frames at 2400 framesper second. The FPGA, ASICs, or one or more microcontrollers canautomatically sample the images into smaller resolution images intosmaller resolution images at fewer frames per second sent to the computeunit. Additionally, it can send more than one of different types ofimages packets to the compute unit such that the compute unit receivesdifferent streams of data captured by the same pair of image sensors.For example, the 8K frame captured at 2400 frames per second can be downsampled to 4K frames at 30 frames per second at the FPGA/ASIC level, andthen sent to the compute unit so that the compute unit can partition atask to analyze 4K image frames at 30 frames per second. In one example,it can partition a task to analyze a stereo pair of 4K image frames at30 frames per second. Additionally, the 8K frame captured at 2400 framesper second can be down sampled to 1080p frames at 240 frames per secondat the FPGA/ASIC level, and then sent to the compute unit so that thecompute unit can separately partition a task to analyze 1080p imageframes at 240 frames per second, both streams of image data coming fromthe same pair of cameras. This would reduce the need for two sets ofstereo cameras enclosed in a single camera bank. The disclosure above isfor illustration purposes only, and the FPGA/ASIC and othermicrocontrollers described can downsample the image stream to any typeof quality and speed to send to the compute units for analysis. Thecamera module itself can account for auto balance, auto white balance,auto exposure, tone, focus, as well as synchronization with the LEDlights including the LED light's exposure, temperature, peak to peakexposure time, as well as perform color correction algorithms to theimages before the images are sent to the compute unit for analysis. EachLED light may be synchronized to turn on and off with respect to when anidentification camera(s) is capturing an image. The number of cameras orother sensing devices, as well as the number of individual LED Lightsare for illustration purposes only. In one example, more than twotreatment units 1110 can be supported by a single modular treatmentmodule 900 or a part of a single modular treatment module 900. Themodular treatment module 900 can include a varying number of sensingenclosures, illumination modules, and treatment units, all operablyconnected to each other as one treatment module similar to that oftreatment module 804.

FIG. 9B illustrates an alternate configuration of an example modulartreatment module 902. The module treatment module 902 can include asupport structure and components supported by or embedded in the supportstructure, including a treatment unit and a treatment unit supportstructure 923, one or more image sensors 918 including a compute unitand image sensor box or enclosure 916, and one or more illuminationunits 920 having one or more LED Lights with one or more lenses.

FIG. 10 illustrates an example method 1000 that may be performed by someexample systems, subsystems, or components of systems, described in thisdisclosure. For example, at step 1010, the agricultural treatment systemcan determine a first real-world geo-spatial location of theagricultural treatment system. At step 1020, the agricultural treatmentsystem can receive one or more captured images depicting real-worldagricultural objects of a geographic scene. At step 1030, theagricultural treatment system can associate the one or more capturedimages with the determined geo-spatial location of the agriculturaltreatment system. At step 1040, the agricultural treatment system canidentify, from a group of indexed images, mapped images, previouslyassigned images, or representations of agricultural objects including atleast in part, image data and position data, or a combination thereof,one or more images having a second real-word geo-spatial location thatis proximate with the first real-world geo-spatial location. At step1050, the agricultural treatment system can compare at least a portionof the identified images with the one or more captured images. At step1060, the agricultural treatment system can determine a target objectbased on the comparing at least a portion of the one or more identifiedimages with at least a portion of the one or more captured images. Atstep 1070, the agricultural treatment system can emit a fluid projectileat a target object in the real-world with a treatment device. The targetobjects are real-world objects that are intended to be sprayed with thefluid proj ectile.

The agricultural treatment system may store the group of images in anonboard data storage unit or a remote storage unit. The group of imagesmay include key frame images and sub-key frame images. The key frameimages may depict agriculture objects of the geographical scene, and thesub-key frame images may depict a portion of a key frame image, forexample a portion of a key frame image can be an image of anagricultural object or cluster of agricultural objects. The key frameimages may be images that were previously obtained by image sensors ofthe system. The captured digital images may be obtained by the samecameras of the system at a time subsequent to when the key frame imageswere taken. For example, in one trial run, the agricultural treatmentsystem, or similar systems 100, 600, and 800, can perform observationsof a geographic boundary including detecting and indexing any and allagricultural objects captured by image sensors, and perform one or moreprecision treatments on detected agricultural objects on the geographicboundary, such as a farm or orchard. The agricultural treatment systemcan index each image captured by its on-board vision system includingone or more image sensors configured to capture images of agriculturalobjects or crops, or offline at a remote computing location nearby thephysical location of the geographic boundary or at different remotelocation such that the remote computing units can communicate with theagricultural treatment system. The indexed series of images captured byimage sensors can be further indexed, where one or more of the capturedimages can be assigned as a keyframe, include a unique keyframe marker.Each keyframe can represent image that include one or more uniqueagricultural object or landmark of interest in the real world. Becauseof the navigation unit of agricultural treatment system, the keyframescan include location data and a timestamp. For example, the agriculturaltreatment system, in a trial, can capture a series of captured images asthe vehicle travels along a path in the geographic boundary. The seriesof images captured can be images taken of a row of plants including rowcrops grown directly from the soil or crops growing off trees. One ormore images of the series of images captured can include agriculturalobjects of interest, either for treatment or for observation where theagricultural object can grow into a stage where it is desirable toselect a treatment for the agricultural object. The agriculturaltreatment system can assign the particular image having the individualagricultural object identified as a keyframe. The keyframe, or any otherimages captured by the agricultural treatment system can include alocation based on image analysis performed by the compute unit of thetreatment system. For example, a stereo vision system can use epipolargeometry to triangulate a location of an object identified in an imagerelative to the location of the image capture device.

Additionally, each portion of the image that includes agriculturalobjects can be labeled and assigned a unique identifier to be indexed ina database. The data indexed can be a 2 d or 3 d constructed image of anagricultural object having a location and position data attached to theimage and a timestamp of when the image was taken. In future trialsconducted by the agricultural treatment system, the agriculturaltreatment system may capture images of the same agricultural object atthe same or similar location in the geographic boundary. Since the imagecaptured of the agricultural object in the same position was acquired ata future time from the previously captured agricultural object, theagricultural object may have grown to have different features. In oneexample, the agricultural treatment system can determine that anacquired image of an agricultural object with location and positiondata, is associated with that of a previously acquired, labeled,assigned, and indexed image or other indexed representation of anagricultural object that is the same agricultural object as thecurrently detected object. Having associated the two images withlocation and timestamp data, the agricultural treatment system candetermine treatment parameters, including whether to perform a treatmentat the given time or trial, determining a mixture, chemical type,volume, concentration, etc., of a treatment, and a precise trajectoryfor the treatment to be deposited on a surface of the agriculturalobject. In one example, a user can select in an application the indexedagricultural object, and a user interface of the agricultural treatmentobject can display information related to the agricultural objectincluding images taken of the agricultural object, including multipleimages taken at different locations, and with orientations of the imagecapture device, for capturing different views of the same agriculturalobject, as well as multiple images taken at different points in time asthe agricultural treatment system conducts multiple trials and capturesimages of the same or near the same location as previously capturedimages.

The above example illustrates the agricultural treatment systemperforming two trials with two sets of images captured at differenttimes, for example a day apart, of the same agricultural object andassociating the images of the agricultural object with each other basedon image features detected that are common between the images, position,depth, localization, and pose related information from image analysisand computer vision techniques, as well as similar position datacaptured by the navigation unit of the agricultural treatment system. Asmore trials are conducted and more images of a same agricultural objectare taken, capturing the agricultural object's current growth stage, andassociating each captured agricultural object with one or morepreviously captured images of the same agricultural object, thetreatment system can build a unique profile of each unique andindividual agricultural object mapped in a geographic boundary,including images associated with each of its growth stages, any and alltreatment history to each individual agricultural object. This can allowa user or a treatment system to determine a crop's health, includingdiseases and stress, for example for fire blight detection, and colorchange, size, count, growth projection, yield projection and estimationof the crop grown on a farm or orchard and allow a user optimize growingcrops on a farm by observing and controlling the growth rate of eachindividual agricultural object detected on a geographic boundary.

In one example, to identify target objects for spraying, the system maycompare at least a portion of the identified images by comparing thesub-key frame image to a portion of one of the captured images. In otherwords, the agricultural treatment system can compare one or more patchesor labeled portions of a previously indexed image of an agriculturalobject with at least a portion of the currently captured image. In thisexample, a patch is an image cropped out of a bigger image having one ormore features of interest. The features of interest in the bigger imagecaptured by image sensors can include agricultural objects, landmarks,scenes or other objects of interest to be identified, labelled, andassigned a unique identifier or marker to be indexed. For example, abounding box of an image, or other shape, can be drawn around a portionof an image, cropped out and separately indexed by the agriculturaltreatment system and saved as a patch for comparing against capturedimages taken in the future, for building a digitized map of a geographicboundary, for associating an object captured during one trial with thesame object captured at different trials, or a combination thereof. Thesystem determines a confidence level of whether the sub-key frame imagematches the portion of the captured image. The system identifies a matchwhere the determined confidence level meets or exceeds a predeterminedconfidence level threshold value. In one example, various computervision techniques can be applied to compare and correspond images anddetermine similar features for matching. This can include templatematching for comparing a portion of an image with the region of interestof another image, normalized cross correlation, random sample consensus(RANSAC), scale-invariant feature transform (SIFT), FAST, edgeorientation histograms, histogram of oriented gradients, gradientlocation and orientation histogram (GLOH), ridge and edge detection,corner detection, blob detection, line detection, optical flow,Lucas-Kanade method, semantic segmentation, correspondence matching, andother computer vision and matching techniques. The system may identifythat a captured image includes a target object to be treated or a targetobject that was already sprayed and does not currently need a treatmentbased on features detected of the agricultural object, based on itstreatment history, or a combination thereof. Based on determining thelocation of the image sensors of the agricultural treatment system, thelocation of the target object in the obtained image, the system can thenconfigure, orient, and prepare the treatment unit such that a fluidprojectile when emitted, would be sprayed in a trajectory to emit fluidonto the real-world targeted agriculture object.

In another example, the system may use landmark features or objects todetermine locations of target objects to be sprayed. The landmarkobjects are real-world objects that aid in determining the location of atarget object. The system may identify a landmark object in a capturedimage and determine a portion of the landmark object in the captureimage matches a portion of an image from the group of images. While notintended to be an exhaustive list, examples of landmark object mayinclude a man-made object, a fence, a pole, a structure, a portion of aplant structure, a portion of a tree structure, a leaf formation or aleaf cluster that can be used to mark a specific location of ageographic boundary or distinguish a specific keyframe for having theunique landmark assigned to the portion of the keyframe.

In another example, in one mode of operation, in a first pass along apath along an agricultural environment, the agricultural treatmentsystem obtains a first set of multiple images while the system movesalong the path. For example, the agricultural treatment system usesonboard cameras and obtains multiple digital images of agriculturalobjects (e.g., plants, trees, crops, etc.). While obtaining the multipleimages of the agricultural objects, the agricultural treatment systemrecords positional and sensor information and associates thisinformation for each of the obtained images. Some of this informationmay include geo-spatial location data (e.g., GPS coordinates),temperature data, time of day, humidity data, etc. The agriculturaltreatment system or an external system (such as a cloud-based service)may further process the obtained images to identify and classify objectsfound in the images. The processed images may then be stored on a localdata storage device of the agricultural treatment system.

In a second pass along the agricultural environment, the agriculturaltreatment system using the onboard cameras obtains a second set ofmultiple digital images using along the path that had been previouslytaken along the first pass. For example, the agricultural treatmentsystem may obtain the first set of multiple images on day 1, with theimages capturing blossoms on a group of apple trees. The digital imagesdepicting the apple trees may be processed for object classification ofthe types of blooms depicted in the digital images. The agriculturaltreatment system may retrieve the processed imagery and associated dataidentifying the objects and classified types. On day 2, the agriculturaltreatment system may again follow the original path and obtain newimagery of the apple trees. The agricultural treatment system may thenuse the second set of obtained images in comparison with the receivedprocessed images to identify target agricultural objects to be sprayed,and then spray the agricultural objects. The system then can match thelandmark objects to aid the system in determining locations of targetobjects. In other words, the system may use feature matching of objectsin the imagery to determine that a prior image is similar to a capturedimage.

For example, the processed images received by the treatment system, mayhave associated positional information. As the agricultural treatmentsystem moves along the path in the second pass, the agriculturaltreatment system may compare a subset or grouping of the processedimages based on location information associated with the processedimages, and a then current position or location of the treatment system.The agricultural treatment system compares new images to the processedimages and determines whether the images or a portion of the images aresimilar. The agricultural treatment system may then identify a locationto spray based on a likely location of a target object in the processedimages.

As noted above, the agricultural treatment system may associate imagescaptured by a camera(s) with real-world physical locations of whereimages of agricultural objects were obtained. For example, while avehicle with an agricultural treatment system is moving along a path, anelectronic control unit of the agricultural treatment system maygenerate camera data signals and light data signals with synchronizedlighting from the lighting devices of the agricultural treatment systemand the capturing of digital images. The ECU may synchronizeillumination, by one or more lights mounted on the vehicle, of thephysical location of an object(s) for generation of the respectivecaptured image(s) that corresponds with that physical location of theobject(s). The object determination and object spraying engine sends thecamera data signals and light data signals to ECU. The objectdetermination and object spraying engine generates position informationthat corresponds with a position and an orientation of the vehicle withrespect to physical location(s) of the agricultural object(s) and acurrent route of the moving vehicle. The position information mayfurther be associated with the respective captured image(s) thatcorresponds with the physical location(s) of the agricultural object(s).

FIGS. 11A-11H illustrate example agricultural treatment units, such astreatment unit 1100 of an agricultural treatment system. In thisexample, the treatment unit 1100 can include a turret assembly with atreatment head 1120, and circuitry, electronic components and computingdevices, such as one or more microcontrollers, electronic control units,FPGA, ASIC, system on chip, or other computing devices, configured toreceive instructions to point and orient the treatment head 1120, totreat a surface of a real-world object in proximity of the treatmentunit 1100. For example, the treatment unit 1100 can emit a fluidprojectile of a treatment chemical onto an agricultural object in thereal world based on detecting the agricultural object in an imagecaptured and determining its location in the real world relative to thetreatment unit 1100.

The treatment unit 1100 can include a gimbal assembly, such that thetreatment head 1120 can be embedded in, or supported by the gimbalassembly, effectively allowing the treatment head 1120 to rotate itselfand orient itself about one or more rotational axes. For example, thegimbal assembly can have a first gimbal axis, and a second gimbal axis,the first gimbal axis allowing the gimbal to rotate about a yaw axis,and the second gimbal axis allowing the gimbal to rotate about a pitchaxis. In one example, the gimbal assembly can have a third gimbal axisto allow roll of the treatment unit, giving the treatment head 120 ofthe treatment unit 1100 a total of 3 degrees of freedom relative to thetreatment unit 1100. In this example, a control module of the treatmentunit can control the gimbal assembly which changes the rotation of thegimbal assembly about its first gimbal axis, second gimbal axis, orboth. A computing module can determine a location on the ground scene,terrain, or tree in an orchard, or other agricultural environment, andinstruct the control module of the treatment unit 1100 to rotate andorient the gimbal assembly of the treatment unit 1100. In one example,the computing module can determine a position and orientation for thegimbal assembly to position and orient the treatment head 1120 in realtime and make adjustments in the position and orientation of thetreatment head 1120 as the treatment unit 1100 is moving relative to anytarget plants or agricultural objects of interest on the ground eitherin a fixed position on the ground, or is also moving. The treatmentsystem can lock the treatment unit 1100, at the treatment head 1120,onto the target plant, or other agricultural object of interest throughinstructions received and controls performed by the control module ofthe treatment unit 1100, to adjust the gimbal assembly to move, or keepand adjust, in real time, the line of sight of the treatment head 1120onto the target plant.

In one example, the treatment unit 1100 can include protectivecomponents such as an enclosure 1170 to shield the treatment unit 1100from dust, water, moisture, sunlight, and other particles that candamage components of the treatment unit 1100, as well as protect thetreatment head 1120 from wind or other forces that could disturb thedesired orientation of the treatment head 1120. The treatment unit 1100can also include one or more mounting brackets 1172 to mount theenclosure 70 supporting the treatment unit 1100 to other components ofthe treatment system or a vehicle supporting the treatment system.

In one example, one or more brushless motors can be configured to changeone axes of the treatment head of the treatment unit. A solenoid valvecan be configured to open or close the valve where constant pressurizedfluid from the pump is pumping fluid into the solenoid valve. Thetreatment unit can emit a projectile by an amount and time the valve ismoved from a closed position to an opened position.

In one example, the treatment unit 1100, having a high-powered laserunit or laser chip embedded in or supported by the treatment unit 1100,can be configured to treat portions of plants that are larger than planttypically only grow a few inches or feet above the ground. These plantscan include trees, orchard trees, or other plants with one or moretrunks, shrubs, bushes, or other plants grown on trellises or otherhuman made mechanisms such that a horizontally or top mounted treatmentunit 1100 is more practical

In one example, the figures illustrate a regulator 1180 (e.g., asolenoid valve) can be configured to open or close the valve where aconstant pressurized fluid from the pump is pumping fluid into theregulator 1180. The treatment unit 1100 can emit a projectile by anamount and time the valve is moved from a closed position to an openedposition.

While the figures illustrate a single solenoid valve 1180, the treatmentunit 1100 may be configured with multiple solenoid valves that areinterconnected with different pumps to obtain fluid from different fluidsources. For example, the agricultural treatment system may have foursource tanks to hold different types of fluids. Four pumps may beutilized, each of which can be fluidly interconnected with a respectivetank and a separate solenoid valve. The pump(s) may pressurize thefluid, for example in a range of 5-250 PSI. For example, when treatingan agricultural object such as carrots, the solenoid may control fluidpressure to about 40 PSI. When treating an agricultural object such asapples, the solenoid may control fluid pressure to about 80-100 PSI. Thesystem may be configured to allow two or more fluid source to be emittedat the same time. For example, the spraying nozzle 1130 of the gimbalassembly 1140 may have multiple spraying ports. Also, the fluid linesmay be connected before or after the solenoid valve to mix differentfluid sources from the tanks. Pressure sensors may be connected alongany part of the fluid channels to determine the pressure of a line.

The system may then increase or decrease the fluid pressure to a desiredpressure via opening/closing of the solenoid valve, and by adjusting thepumping of the fluid via a pump. The size and volume of each sprayemitted by the spraying nozzle 1130 may be adjusted by the system basedon the targe object to be sprayed. For example, the system may spray1.25 mL at 80 PSI for one agricultural object, and at a different volumeand PSI for another agricultural object. Moreover, the spray radius maybe determined as a distance from the spray nozzle to the targetedobject. For example, the spray radius may be about ⅛″-5″ diameter with acoverage at about 1 meter with a +/−25-degree sweep at 0.094m.Additionally, the spray time may be adjustable and intermittent sprayburst of fluid may be controlled by the system.

In one embodiment, the system may adjust the operation of the solenoidto release the pressurized fluid to create a desired spray diameter whenthe spray hits the target agricultural object. For example, the systemmay adjust the operation of the solenoid so that a target object 3 feetaway from the spraying head and a target object 6 feet away from thespraying head would each have a similar spray diameter. Also, the spraydiameter may be adjusted such that the diameter of the spray for thecloser target object may be a greater or lesser diameter of the spray ofthe target object farther away from the spraying head. In this example,the system may determine how far away the nozzle of the treatment unitis from the ground, determine what the target object is, the size of thetarget object and how much (e.g., an area) of the target object totarget. The system may target a portion of an agricultural object, suchas a root, a leaf, a bud, etc. The agricultural object may be repeatedlysprayed with fluid from the same and/or separate fluid sources.

In one example, the gimbal assembly 1140 has a detachable sprayingnozzle 1130. One or more fluid carrying tubes 1182 may be connected tothe gimbal assembly 1140 for carrying fluid from one or more fluidregulators 1180 to be emitted via the spraying nozzle 1130. The sprayingnozzle 1130 may have a threaded base portion allowing the sprayingnozzle 1130 to be attached to gimbal assembly 1140. In some,configurations the spraying nozzle 1130 has multiple ports that areconnected to different fluid lines. This allows the gimbal assembly 1140to emit fluid pumped from different tanks or fluid sources. For example,the spraying nozzle 1130 may have one, two, three or four ports in thespraying tip. Also, multiple ports of the spraying tip may be connectedto the same fluid source. For example, the spraying nozzle 1130 may havetwo ports. The two ports may be configured such that fluid emitted attarget object would spray the target object at two different locations.The spraying nozzle 1130 can be various types of tips, including forexample a hypodermic needle with one or more input ports or orifices fordifferent types of fluids, and with one or more output ports ororifices.

In one example, the system may determine a target object to be of aparticular type. Based on the particular type of the target object, thesystem may choose to treat the target object with a fluid source fromone or more tanks. For example, the system may determine the targetobject to be a weed and then cause fluid from a first tank to be emittedfor weed treatment. In another instance, the system may determine thetarget object to be a plant and then cause fluid (e.g., liquidfertilizer) from a second tank to be emitted at the target object.

This treatment unit 1100 is capable of emitting a projectile fluid at atarget object in a continuous manner over a period of time, such as manyseconds or minutes. The treatment unit may also emit short intermittentbursts of a projectile fluid at a target object. The spraying nozzle1130 of the gimbal assembly 1140 may be configured to emit theprojectile fluid in a stream-like manner where the fluid is kepttogether in a stream to impact a target object in a focused area. Thespraying nozzle 1130 of the gimbal assembly 1140 may be configured toemit the projectile fluid in a spray-like manner where the fluid isfanned out or the fluid separated to impact a target object in a generalarea. The gimbal assembly 1140 may be configured with one or morespraying tips and/or spraying ports that allow for the emission ofeither type of fluid spray types. The spraying of fluid from thespraying nozzle 1130 is variable and adjustable based on the targetobject. In one example, for spraying carrots the distance from thespraying tip to the target object may be about 27 inches. In anotherexample, for spraying apples the distance from the spraying tip to thetarget about may be about 0.5-1.5 meters. In one embodiment, the fluidmay be emitted in a short pulse of less than 10 bursts per second. Inanother embodiment, the fluid may be emitted in a volume of fluidranging from 1 milliliter to 1 liter. In another embodiment, the fluidmay be emitted in a continuous volume of sprayed fluid over a period of5 milliseconds to 300 seconds.

The agricultural treatment system may use a controller 1190 to interactwith, obtain sensor data and control the motors and the solenoids. Inone embodiment, to instruct the gimbal assembly 1140 to a determinedpose, the system instructs the motors 1152 to rotate in one direction orthe other. Pose determination is described further below. The system mayinstruct the motors 1152 to rotate at a constant speed or a variablespeed. The system may vary the sample rate based on the rotation speedof the motor, the movement speed of the vehicle, or based on some othervalue or data obtained by the system. When the system determines thatthe gimbal assembly 1140 is moved to a desired pose, then the system mayinstruct the fluid regulator 1180 to open, allowing fluid to flowthrough the spay tube 1182, and emit a fluid at a target object via thespray nozzle 1130. The system may then instruct the regulator 1180 toclose thereby stopping the fluid from being emitted from the sprayingnozzle 1130. As described herein, when the either motor 1152 rotates,the coupled linkage assemblies 1154, 1160 cause the gimbal assembly 1140to rotate or pivot in a particular direction. For example, the systemmay be configured that when a first motor rotates in a first direction,the gimbal assembly 1140 would pivot or move along a first axis in afirst direction, and when the first motor rotates in an opposite seconddirection, the gimbal assembly 1140 would pivot or move along the firstaxis in a second direction opposite to the first direction. Moreover,the system may be configured that when a second motor rotates in a firstdirection, the gimbal assembly 1140 would pivot or move along a secondaxis in a first direction, and when the second motor rotates in anopposite second direction, the gimbal assembly 1140 would pivot or movealong the second axis in a second direction opposite to the firstdirection.

In one example, one or more brushless motors can be configured to changeone axes of the treatment head of the treatment unit. A solenoid valvecan be configured to open or close the valve where constant pressurizedfluid from the pump is pumping fluid into the solenoid valve. Thetreatment unit can emit a projectile by an amount and time the valve ismoved from a closed position to an opened position.

FIGS. 12A-12B illustrate example implementations of method 1200 that maybe performed by some example systems, subsystems, or components ofsystems, described in this disclosure. For example, in one mode ofoperation, at step 1210, an agricultural treatment system can receiveimage data from one or more sensors, the image data including one ormore agricultural objects. The one or more agricultural objects can beidentified as one or more target plants from the image data. At step1220, the agricultural treatment system can receive agricultural datarepresenting agricultural objects including different crops and targetplants. At step 1240, the agricultural treatment system can identify alocation of the target plant. At step 1250, the agricultural treatmentsystem can determine treatment parameters of the target plant. At step1260, the agricultural treatment system can compute a vehicleconfiguration and treatment unit configuration for treating the targetplant. At step 1270, the agricultural treatment system can lock thetreatment unit onto the target plant in the real world. At step 1280,the agricultural treatment system can activate the treatment unit andemits a fluid projectile of a treatment chemical onto the target plant.

Additionally, the agricultural treatment system can receive, fuse,compute, compensate, and determine positional, localization, and poserelated signals on a geographic boundary. At step 1212, the agriculturaltreatment system can receive sensor data, from one or more sensors on avehicle of an agricultural environment. The agricultural environment canbe that of a geographic boundary having a plurality of objects typicallyfound on a farm or orchard for cultivating land and growing andharvesting crops. At step 1214, the agricultural treatment system canidentify a vehicle position, one or more agricultural objects inproximity of the vehicle, and determine distances of the vehicle to theagricultural objects. At step 1216, the agricultural treatment systemcan calibrate the vehicle, including calculating a pose estimation ofthe vehicle relative to a central or known point in the geographicboundary, pose estimation of components of the agricultural treatmentsystem relative to the vehicle supporting the agricultural treatmentsystem, or agricultural objects detected in space relative to thevehicle. The vehicle can be calibrated by locating one or morecalibration targets spread throughout a mapped geographic boundary suchthat as the agricultural treatment system identifies a physicalcalibration target and calculates its position relative to thecalibration target, the agricultural treatment system can determine, orcorrect a previous inaccurate determination, a position of the vehiclein the geographic boundary.

FIGS. 13A-13B illustrate example images obtained by an agriculturalobservation and treatment system described in this disclosure. As shownin the diagram 1300 a of FIG. 13A, an image received by an agriculturaltreatment system may include multiple identifiers of different types ofobjects, for example objects 1302, 1304, 1306, 1308, and/or 1310, of aplurality of objects detected, each having different identifiersportrayed in a captured image. For example, an identifier marked forobject 1306 or 1308 may identify a portion of the captured image thatportrays a physical landmark of an of an agricultural object orlandmarks in an agricultural scene. The object 1306 may further be basedon visual characteristics of the object.

The diagrams 1300 a and 1300 b representing images with one or moredetections can either be ingested images by a compute unit of acomponent treatment module with machine learning, computer vision, orboth, based detections performed by feature extraction and objectdetections in real time while the treatment module is scanning anenvironment, or representing images with labels performed by humanlabelers, machine learning detections, or a combination thereof where amachine learning detector scans and detects objects and landmarks in agiven frame, and a human labeler verifies the quality of the detectionsand manually labels missing or incorrectly classified objects.

FIG. 13B illustrates another image 1300 b depicting another examplereal-time captured image with real-time object detection or a receivedlabelled image having the labelling of objects in the received imagedone offline from the portion of agricultural observation and treatmentsystem supported by a vehicle. Additionally, diagram 1300 b illustratesexample portions or sub-images of an image obtained by an agriculturaltreatment system.

In one example, diagram 1300 b is a labelled image, either fromreal-time performed by an agricultural observation and treatment systemon the vehicle, or offline at a server, by a human, by a machinelearning algorithm, assisted by a machine learning algorithm, or acombination thereof.

Based on visual characteristics of an instance of an apple blossomportrayed by the captured image of an apple tree, the labeled image mayinclude an identifier 1302 b for the apple blossom instance. Theidentifier 1302 b may be positioned in the labeled image 1300 b at afirst pixel position that corresponds to the apple blossom instance'sphysical location as it is portrayed in the captured image of the appletree. Based on visual characteristics of an instance of an applefruitlet portrayed by the captured image of the apple tree, the labeledimage may include an identifier 1310 b for the apple fruitlet instance.The identifier 1310 b may be positioned in the labeled image at a secondpixel position that corresponds to the apple fruitlet instance'sphysical location as it is portrayed in the captured image of the appletree. Based on visual characteristics of an instance of a landmarkportrayed by the captured image of the apple tree, the labeled image mayinclude an identifier 1308 b for the landmark instance, the specificlandmark identifier 1308 b being that of two branches diverging in thespecific pattern, shape, and orientation illustrated in 1300 b. Theidentifier 1308 b may be positioned in the labeled image 1300 b at athird pixel position that corresponds to the landmark instance'sphysical location as it is portrayed in the captured image of the appletree.

In one example, to perform better VSLAM in an agricultural scene,certain objects that are landmarks that are tracked across time canimprove the quality of VSLAM and pose estimation, for example, largeenough stationary objects typically found in the specific agriculturalscene. Landmarks can be used to identify which frames are of interest tostore, store as a keyframe (because one does not need so many frames atonce all having the same fruits, or detected objects, from frame toframe), and to be used to identify objects in real time and tracked forvisual based navigation and mapping including VSLAM. Because there arespatial locations to each of the objects, landmarks, and it' uniqueidentifying characteristic. In one example, tree trunk 1336 can bedetected, by a machine learning algorithm or programmatically predefinedas stationary dark objects that protrude from the ground. Detection andtracking tree trunks in an orchard can allow a system to partition anagricultural environment by the trees themselves, such as to minimizeerror in detecting one cluster of objects and thinking its origin is atonce place, when it should be at another. For example, a system candetect a first tree trunk having a first location in global scene, aswell as determine a pose of the system itself relative to the tree trunkdetected. The system will also detect a plurality of objects, includingits identity as well as whether that unique object was detected beforeeither with the same identifier, or a different identifier, being thatthe phenological state of the object has changed, but still the sameobject in space. In this example, the system can associate a cluster ofobjects detected, being on the same tree, with the tree trunk detected.In this case, if the system incorrectly detects other objects orlandmarks at different and nearby trees due to its pattern being similarto a previously identified pattern or object, and it's location basedsensors are not accurate which the change in location was not detectedfrom a first object, pattern or landmark located near a first tree trunkand a second object, pattern, or landmark located at a second treetrunk, for example if the GPS sensor is off by a few meters or did notupdate in time, An additional checking point for the system can bedetecting a first tree trunk and a second tree trunk. Because the systemknows that two different tree trunks must be far enough apart from eachother, the system can determine that a previously detected objectdetermined to be a certain location is likely wrong due to the systemalso determining that the object detected was in proximity to anothertree trunk that could not have been located at a different location.

While tree trunks are unique to orchards, any large, stationary objectsor patters that are unique to the specific geographic environment can beprogrammatically detected to better improve spray performance,navigation performance, and mapping of the scene. For example, detectingbeds, troughs, furrows, and tracks of a row crop farm can be used toimprove performance of observing and performing actions in the row cropfarm. The techniques used can be a combination of computer vision,machine learning, or machine learning assisted techniques in detectingbeds, troughs, furrows, and tracks such as long lines in a capturedframe, differences in depth between lines (for example tracks and bedswill have substantially the same line pattern because they are next toeach other but have different depths), which can be detected with depthsensing techniques and detecting changes in color between beds andtracks, for example.

In one example, the object determination and object spraying enginegenerates positional data for an instance of the fruit at a particularstage of growth that is portrayed in a captured image based in part on:(i) a pixel position of the portrayal of the instance of a fruit at theparticular stage of growth in the labeled image (and/or the capturedimage), (ii) the position information of the moving vehicle, and/or(iii) previously generated position information associated with aprevious captured image(s) of the instance of the fruit and the physicallocation of the instance of the fruit. Previously generated positioninformation may be associated with captured and labeled images thatportray the same instance of the fruit when the vehicle traveled asimilar route during a previous time, such as a prior hour of the day,prior day, week and/or month. The agricultural treatment system maygenerate nozzle signals for the synchronization ECU of the agriculturaltreatment system on a vehicle based on the positional data for theinstance of the fruit at the particular stage of growth. For example,the nozzle signals may indicate a physical orientation of the nozzle tocreate a trajectory for a liquid. The nozzle signals may represent achange in a current orientation of the nozzle based one or more axialadjustments of the nozzle.

The object determination and object spraying engine sends the projectilefrom the nozzle towards the physical location of the object according tothe trajectory. For example, the object determination and objectspraying engine adjusts a current orientation of the nozzle according tothe nozzle signals and triggers the nozzle to spray a liquid towards thephysical location of the instance of the fruit.

Because not all plants need the same amount, for example by type,volume, frequency, or a combination thereof, of treatment based on thestage of growth of the particular plant, the agricultural treatmentsystem can be configured to scan a row of crops to identify the stage ofgrowth of each individual crop or agricultural object that is a plant orportion of a plant and determine whether the identified crop oragricultural object needs a treatment on the particular trial run, orday, or at the particular moment in time the vehicle with agriculturaltreatment system is on the field and has detected the individualagricultural object. For example, a row of crops, even of the same kindof plant, can have a plurality of agricultural objects andsub-agricultural objects of the agricultural objects, where theagricultural object may depict different physical attributes such asshapes, size, color, density, etc.

For example, a plant for growing a particular type of fruit, in oneagricultural cycle, can produce one or more individual crop units, forexample a fruit tree, each taking the shape of a first type of bud,second type of bud, and so forth, a flower, a blossom, a fruitlet, andeventually a fruit, depending on a growth stage of a particular crop. Inthis example, the agricultural treatment system can label each stage ofthe same identified object or crop, down to the particular individualbud, on the fruit tree as different agricultural objects or subagricultural objects, as the object changes in its growth stageincluding its particular shape, size, color, density, and other factorsthat indicate a growth into a crop. The different agricultural objectsdetected and labelled associated with the same object in the real-worldspace can be associated with each other

For example, a bud detected can be labelled as a unique agriculturalobject with a unique identifier or label. As time moves forward in aseason, the uniquely labelled bud that is mapped on a farm may changeshape into a flower for pollination, or from a flower to a fruitlet, andso forth. As this happens, an agricultural treatment system can identifythe flower and label the flower as a unique identifier to theagricultural object detected and associate the agricultural object thatis the flower with the agricultural object that is the bud previouslyidentified and logically link the two identified agricultural objects asthe same object in the real world where one object identified has growninto the other. In another example, the unique real-world flowerdetected, of a plurality of flowers and other objects in a geographicboundary, can be labelled as a flower but not considered a differentagricultural object, and instead be associated with the sameagricultural objected previously labeled as a bud. In this example, eachobject detected that can be considered a potential crop can be mapped asthe same agricultural object, even though the agricultural object willchange shape, size, density, anatomy, etc. The same agricultural objectdetected in the same space at different times can then have differentlabels and identifiers as related to the stage of growth. For example, afirst agricultural object in space, detected by the agriculturaltreatment system, can be identified and indexed as a real-worldagricultural object #40 with a timestamp associated with the time of dayand year that the agricultural treatment system captured one or moreimages or other sensing signals of agricultural object #40. At themoment in time of identification, the agricultural object #40 can have afirst label and assign the first label to agricultural object #40. Thefirst label can be labelled as a bud, or bud #40 since there may be manyother buds detected in the geographic boundary such as a farm ororchard. As multiple trials across a span of time are conducted in thegeographic boundary on the same agricultural object #40, theagricultural object #40 can turn from a first type of bud, such as adormant bud, into a second type of bud, or from a bud and bloom into aflower, or many other changes in stages of growth of desiredagricultural plants grown for harvest and consumption. In this example,the agricultural object #40 detected as a bud at a given moment in timecan be labeled as agricultural object #40 as a first label of bud #40.As time moves forward in a season, the agricultural objects on the farmor orchard, including agricultural object #40 as bud #40 can naturallyturn into a flower. At this moment, if and when the agricultural object#40 turns into a flower, the agricultural treatment system can label theagricultural object #40 as a flower #40, associating the bud #40 withflower #40 such that the bud #40 and flower #40 are the sameagricultural object #40 in the real world. Not all agricultural objectsdetected of the same plant may experience the same stages of growth orcontinue to keep growing. Some agricultural objects may even be removed,for example by thinning. For example, some plants can be thinned suchthat one or more agricultural objects growing from a single tree or stemcan be removed or treated such that the next growth stage will nothappen. In this instance, the agricultural treatment system can stilldetect that a uniquely identified real world agricultural object did notreach, or stopped, at a certain growth stage having unique physicalfeatures for a unique object label, or that the agricultural objectdetected previously is now gone and cannot be detected by theagricultural treatment system due to thinning or other method ofremoving the agricultural object so that neighboring agriculturalobjects can continue to grow as desired.

The description of buds, blooms, flowers, fruitlets, and otheragricultural objects and stages of growth of such agricultural objectsdiscussed are only meant to be an example series of objects that can bedetected by a treatment system, such as agricultural treatment systemdetecting fruits and objects associated with the stages of growth offruits on fruit trees, and not meant to be limiting only to the specificexample described above.

For example, as illustrated in FIGS. 13A and 13B, an image depicting anagricultural environment including a fruit tree having one or morespurs, one or more branches and stems, one or more laterals, and one ormore potential crops growing on the one or more laterals. At the momentan agricultural observation and treatment system, or an agriculturaltreatment system, described throughout this disclosure, has observed andlabelled each identifiable feature of the image, including detectingagricultural objects and labelling its growth stage, detecting andlabelling landmarks including orientations of portions of the treegrowing including configurations of leaves, branches, physical manmadematerials that can be detected in the image, or other objects and sightsof interest in the image that is not a potential crop, the agriculturaltreatment system can detect that not all identified objects in the imageinclude agricultural objects of the same growth stage. For example, someagricultural objects detected are labelled as buds, some as blossoms,and some as fruitlets. Each of these labels are of agricultural objectsof interest to observe and potentially treat, but not necessarilytreated the same way depending on the growth stage. The agriculturaltreatment system can then determine treatment parameters in real time totreat each individually labelled agricultural object with differenttreatment parameters, or refrain from treating an agricultural object.For example, if a first labelled growth stage does not need to betreated, a second growth stage does need to be treated at least once, athird growth stage does not need to be treated, the agriculturaltreatment system can scan through a path, capture images such as the onedepicted in image 1300 a, and treat only the second labelled growthstage. In this specific example, a blossom can be treated withartificial pollen. The agricultural treatment system can detect thatthere are buds that have not yet blossomed, and fruitlets that havealready grown after the blossom, so the agricultural treatment systemwill refrain from treating the agricultural object 1302 and only treatagricultural objects labelled with the same label as that ofagricultural object 1310. In one example, the agricultural treatmentsystem can select different treatment mixtures and emit differenttreatment projectiles by volume, concentration, mixture type, as well asthe type of emission which can be a single spray projectile, a sprayprojectile with a large surface area travelling towards the surface ofthe agricultural object, or a mist or fog type spray treatment. In thisexample, multiple identified agricultural objects at different growthstages can require a treatment with different parameters. Instead ofrefraining from treating one type of agricultural object at a certaingrowth stage while treating other agricultural objects having thedesired growth stage for a particular trial, the agricultural treatmentsystem can treat multiple types of growth stages of agricultural objectsgrowing on the same tree simultaneously by selecting and receiving adesired chemical mixture for treatment in real time.

The agricultural treatment system can observe, by running a plurality oftrials, such that one trial is a sequence of capturing sensor data,depositing treatments, or a combination thereof, along each row of cropson a farm or orchard one time and captures sensor data and has theopportunity to deposit a treatment for each crop or agricultural objectdetected. For example, a trial run, where the agricultural treatmentsystem scans through a farm of one or more row crops in one cycle, canbe performed once a day, or twice a day, once during daytime and onceduring night time in a calendar day. For example, the agriculturaltreatment system can perform multiple trials or runs on a farm ororchard in a single day, particularly if the growth sequence of a plantis more rapid in one season or series of days over another season, suchthat the agricultural treatment system can capture more changes instages of growth by conducting more trials as well as depositingtreatments onto surfaces of desired agricultural objects morefrequently.

Additionally, each row of crops, whether each row includes the sameplant or of different plant types, for example planted in an alternatingpatter, can include a plurality of plants that have one or more budsexposed, a plurality of plants that have one or more blossoms exposed, aplurality of plants that have one or more fruitlets exposed fortreatment, or a combination of plants having a combination of buds,blossoms, fruitlets, etc., exposed at the same time on a single row. Inthis example, different agricultural objects at different stages willrequire different treatments at different volumes and frequencies. Theagricultural treatment system can identify the particular stage ofgrowth of each uniquely identified agricultural object mapped in the rowof plurality of agricultural objects and give a label or identifier toeach agricultural object based on its different and unique growth stage.The agricultural treatment system can then identify the appropriate ordesired treatment parameters including treatment chemical mixture,density and concentration, whether a treatment is needed at all for theparticular trial if the agricultural treatment system can identify thata particular agricultural object was already previously treated with atreatment deposition such that another treatment at a given trial can betoo close in time for the same treatment to be applied again to the sameunique agricultural object in the geographic boundary, depending on thestage of growth detected.

The agricultural treatment system can detect a first agricultural objectof a plurality of agricultural objects in a row of plants inside ageographic boundary such as a farm or orchard. The agriculturaltreatment system can determine that the first agricultural object isdifferent from a plurality of other agricultural objects by type or thatthe first agricultural object detected is among a plurality of the sametype of agricultural objects as that of the first and can be indexed bya unique identifier to identify the particular object in the real worldso that each unit or object in the real world of the same agriculturalobject type can be indexed and located in the geographic boundary. Forexample, a first agricultural object of a plurality of agriculturalobjects of the same plant type of the same tree or root can beidentified on an orchard or row farm. The first agricultural object canbe assigned and indexed as agricultural object #400 with a uniqueidentifier that identifies its object type, such as a type of crop, andits location in the geographic boundary and time that the identifier wasassigned to the first agricultural object. The agricultural treatmentsystem can also assign a label of the first agricultural object based onthe size, shape, color, texture, etc., with a first label, for examplefruitlet #400 if the detected first object is a fruitlet of a crop.Because different stages of growth of a same desired plant or crop canrequire a different type, frequency, volume, or a combination thereof oftreatment, the agricultural treatment system can determine treatmentparameters, in real time upon detecting the first agricultural object inspace and the growth stage of the first agricultural object eitherdetermined in real time or determined based on the growth stage detectedon a previous trial. For example, if the first agricultural objectdetected at a particular time is a flower or cluster of flowers, theagricultural treatment system can label the flower detected in one ormore images as a flower and determine treatment parameters for theflower. The agricultural treatment system can apply the same type,mixture, amount, and frequency of a treatment to the each of the sameagricultural object type detected at the same growth stage along thesame row of plants. The agricultural treatment system can apply adifferent type, mixture, amount, and frequency of a treatment to each ofthe same agricultural object type detected at a different growth stagealong the same row of plants. In one example, the different growth stageof the plant or portion of a plant can vary by days or hours in one partof a season and vary by weeks or months in another part of a season. Forexample, a tree of a plurality of trees in a row of the same type ofplant yielding the same crop can have portions of the tree, for exampleshoots, spurs, stems, laterals, or branches with nodes, clusters, buds,or other objects for crops, growing at different stages. A bud for apotential crop can form on one portion of the tree or lateral whileother portions of the tree do not have buds. At this stage, theagricultural treatment system can identify the portions of the tree thatdo have buds and perform any treatment including chemical treatment orlight treatment (e.g. laser) that is appropriate for treating a bud of acertain plant. In another example, a tree can have some laterals thathave blossoms and some laterals that only have buds. In this example,the blossoms may be treated with a certain treatment and the buds may betreated with a different type of treatment as that of the treatment forblossoms. The agricultural treatment system can identify and distinguishbetween the various agricultural objects in space having differentlabels based on their growth stage and apply a treatment appropriate foreach unique agricultural object identified and located in the realworld.

The agricultural treatment system can also identify and index atreatment history on each unique agricultural object identified in spaceof a geographic boundary. For example, one or more buds detected onlaterals of a tree can be treated with a certain type of chemical orlight treatment. At this point in time, certain laterals will havelaterals that have yet to form buds. As time moves forward and theagricultural treatment system engages the row of crops for treatment,the laterals that have yet to form buds may now have buds. Additionally,the previously detected buds, that have been treated have not yet turnedonto a flower, or even further stage of a bud that may require anadditional treatment or different type of treatment. In this example,because the agricultural treatment system has indexed each agriculturalobject detected by its growth stage, with a label across time, andtimestamp for each time the agricultural object was detected and itsspecific growth stage and image of the growth stage labeled, theagricultural treatment system can determine which agricultural objectsin the row requires treatment and which agricultural objects in the rowdoes not require a treatment, either because it was already treated in aprevious trial and does not need a treatment every trial, or has notreached a later growth stage detected that will require a differenttype, frequency, mixture, etc., of treatment.

As with the earlier example, the first real-world agricultural object#400, having one or more images, a location, and object type associatedwith object #400, based on its labelled stage of growth, for examplelabel #400, can require a first treatment having a specific treatmentmixture, type, volume, concentration, etc., and projectile emissionstrength. A second agricultural object #401, in proximity toagricultural object #400, for example, being a potential object forharvest of the same tree as that of agricultural object #400, having oneor more images, a location, and object type associate with theagricultural object #401, based on its label #401, can require a secondtreatment having a specific treatment mixture, type, volume,concentration, etc., and projectile emission strength. The difference intreatment parameters such as the mixture, type, volume, concentration,strength of the projectile emitted, or a combination thereof, orabstaining from depositing a treatment at all for the particular trialrun conducted by the agricultural treatment system, can be based on thedifferent growth stage detected, even if the agricultural object is ofthe same type. In one example, different treatment parameters can beapplied to a row of crops with the same type of plant but portions ofthe plant, such as various laterals can have agricultural objectsgrowing on the laterals at different stages and require differenttreatments. Different treatment parameters can be applied to a row ofcrops with different plants in the row, for example with alternatingcrops. In one example, the same treatments with the same treatmentparameters can be applied to the same row of crops of each agriculturalobject having the same or similar stage of growth. In one example, adifferent concentration or frequencies of treatments deposited can beapplied to a row of crops of either the same plant of different plantsat different stages of growth. For example, a first bloom of a lateralcan require one deposition of chemical-#1 with a certain mixture,concentration, volume, etc. Other portions of the tree or other lateralsmay not have yet experienced a bloom from the buds so only the firstbloom will receive a treatment of chemical-#1. At a later time, and morespecifically, at later trial performed by the agricultural treatmentsystem, other laterals will experience a bloom, such as a second bloom.In one example, it would be desirable for the second bloom to receive asingle treatment of chemical-#1. Since the first bloom already receiveda treatment of chemical-#1 and for this particular example growth stageof this particular plant type, this example first bloom only requiresone treatment of chemical-#1, the agricultural treatment system candetect that the agricultural object of the second bloom requires atreatment of chemical-#1 of a specified volume, concentration, strengthof projectile and apply the treatment of chemical-#1, and detect thatthe agricultural object of the first bloom does not need a treatment atall for this trial.

For example, a treatment module, with one or more image sensors in realtime, can sense and detect both object 1302, for example a fruitlet, aswell as object 1308, which is a landmark. In one example, a landmark cana specific pattern detected and indexed in the geographic scene, forexample of a tree pattern branching into two branches. As the vehiclemoves forward in a row of an orchard, the treatment module's imagesensors translates and moves relative to the tree, for example fromright to left, and scans the tree illustrated in 1300 a in real time. Asthe treatment module, with its compute unit, detects objects in the treewhile the vehicle is moving, the treatment module can track both theobject 1302 for targeting, tracking, and treating via the treatmentunit, as well as track the landmark object 1308 to generate and obtain ahigher accuracy motion estimation. In this example, the detecting, vianeural network or computer vision methods such as template matching,correspondence matching, homography estimation, etc. or a combinationthereof, and tracking of the target object can be done for treatment butcan also be tracked for the motion estimation of the treatment module,and by extension the treatment unit and its treatment head, itself. Theaddition of tracking other objects, including other target objects,landmarks that are real world objects, or real-world objects or salientpoints in an image that can be tracked, can add accuracy for poseestimation of the treatment module which reduces error or misalignmentof treatment when the treatment module's compute unit sends instructionsto the treatment unit for treatment.

FIGS. 14A-14B illustrate example implementations of method 1400 that maybe performed by some example systems, subsystems, or components ofsystems, described in this disclosure. For example, in one mode ofoperation,

at step 1410, the agricultural treatment system can obtain a first setof multiple images, on a vehicle, depicting one or more agriculturalobjects along a path.

At step 1420, the agricultural treatment system can receivelocalization, velocity, and acceleration data of the vehicle.

Additionally, at step 1480, the agricultural treatment system cangenerate a pose estimation of the vehicle in a geographic boundary, theagricultural treatment system supported by the vehicle relative to thevehicle, or agricultural objects or other objects detected in thegeographic boundary.

At step 1430, the agricultural treatment system can process the set ofmultiple images to classify objects within the image. The classificationcan be performed on board the vehicle at the agricultural treatmentsystem. Additionally, the image can be processed with computer visiontechniques, image analysis, and machine learning algorithms includingdeep neural networks for performing feature extraction, objectclassification, object detection, and object tracking.

At step 1440, the agricultural treatment system can identify a locationof the one or more agricultural objects classified. At step 1450, theagricultural treatment system can determine one or more treatmentparameters for treating the agricultural object.

Additionally, the agricultural treatment system can detect, target,track, and determine treatment parameters based on previously identifiedand indexed information of one or more images, including one or moreimages with the same agricultural object identified and classified inthe first set of multiple images.

For example, at step 1442, the agricultural treatment system can receivea second set of mapped images, depicting one or more localizedagricultural objects, each with a timestamp associated with the imagecaptured of each mapped image and treatment history associated with eachof the localized agricultural objects.

At step 1444, the agricultural treatment system can compare andcorrespond the classified agricultural objects with the one or morelocalized agricultural objects. The agricultural treatment system canthen activate a treatment unit and emit a fluid projectile onto thetarget plant that is the agricultural object.

FIG. 15 illustrates an example method 1500 that may be performed by someexample systems, subsystems, or components of systems, described in thisdisclosure either online, that is onboard a vehicle supporting one ormore modular agricultural observation and treatment systems, subsystems,or components of systems, or offline, that is at one or more servers oredge compute devices.

For example, in one mode of operation, at step 1510, an agriculturaltreatment system can receive image data in a real-world agriculturalscene from one or more image capture devices. At step 1520, theagricultural treatment system can detect one or more agriculturalobjects in a first image of the image data. At step 1530, theagricultural treatment system can identify a stage of growth associatedwith a first agricultural object. At step 1540, agricultural treatmentsystem can assign a label of the stage of growth of the firstagricultural object. Additionally, at step 1542, the agriculturaltreatment system can index and store the first image with the labelledagricultural object with a timestamp of the first image taken associatedwith the label. At step 1550, the agricultural treatment system candetermine one or more treatment parameters based on the assigned labelof the stage of growth of the first agricultural object. At step 1560,the agricultural treatment system can receive instructions for atreatment unit, of the agricultural treatment system, to receive a fluidmixture from a chemical selector to prepare a treatment. At step 1570,the agricultural treatment system can orient the treatment unit totarget the first agricultural object in the real-world agriculturalscene and activate the treatment unit to emit a fluid projectile at asurface of the first agricultural object.

In one example, the agricultural treatment system can determine thatdifferent chemical concentrations of a chemical mixture are required fordifferent growth stages of the same plant on a row of plants. In oneexample, the agricultural treatment system can determine that differentchemical concentrations of a chemical mixture are required for differentgrowth stages of different plants planted on a same row on a farm ororchard. In another example, the agricultural treatment system candetermine that only certain growth stages of agricultural objects detectrequire a deposition of a particular treatment, and that otheragricultural objects detected require a deposition of a differenttreatment, or no treatment, depending on the stage of growth andtreatment history of the particular agricultural objected detected inthe real world. In one example, a row of plants can have lateralssupporting different agricultural objects, or the same agriculturalobjects with different stages of growth and different treatmenthistories, such that different treatments are desired for each uniqueagricultural object in the row. The chemical selection unit can mixdifferent treatment mixtures and concentrations in real time for theagricultural treatment system to accommodate the different requirementsof treatments in real time while performing a trial in a particular rowof plants. Additionally, the agricultural treatment system canaccommodate for applying different treatments to different agriculturalobjects of different plants in a single row, or other configuration, ofcrops.

Thus, the agricultural treatment system can, in real time, scan withsensors for agricultural objects and its stage of growth and real-worldlocation in the row, determine whether to apply a particular treatmentbased on stage of growth detected and the particular agriculturalobject's treatment history.

In one example, the agricultural observation and treatment system can beconfigured to detect objects in real time as image or lidar sensors arereceiving image capture data. The treatment system can, in real time,detect objects in a given image, determine the real-world location ofthe object, instruct the treatment unit to perform an action, detect theaction (discussed below), and index the action as well as the detectionof the object into a database. Additionally, the treatment system, at aserver or edge computing device offline, can detect objects in a givenimage, spray projectiles, spray action, spot of splat detections, andindex the object detections and spray action detections. In one example,the agricultural observation and treatment system can perform and usevarious techniques and compute algorithms for perform the objectdetections including computer vision techniques, machine learning ormachine learning assisted techniques, or a combination thereof inmultiple sequences and layers such that one algorithm partitions a givenimage and a second algorithm can analyze the partitioned image forobjects or landmarks.

In one example, a machine learning model, embedded in one or morecompute units of the agricultural observation and treatment systemonboard a vehicle, can perform various machine learning algorithms todetect objects, including object detection including feature detection,extraction and classification, image classification, instanceclassification and segmentation, semantic segmentation, superpixelsegmentation, bounding box object detections, and other techniques toanalyze a given image for detecting features within the image. In oneexample, multiple techniques can be used at different layers or portionsof the image to better classify and more efficiently use computerresources on images. Additionally, pixel segmentation can be performedto partition colors in an image without specific knowledge of objects.For, example, for row crop farming, a system can perform colorsegmentation on a given image to partition detected pixels associatedwith a desired color from any other pixels into two groups, such as thecolor segmented pixels and background pixels. For example, a system canbe configured to analyze frames by detecting vegetation, which can be aform of green or purple color from background objects, such as terrain,dirt, ground, bed, gravel, rocks, etc. In one example, the colorsegmentation itself can be performed by a machine learning modelconfigured to detect a specific type of color in each pixel ingested byan image sensor. In another example, the color segmentation can bemanually predefined as pixels ranging between a specific range of acolor format. For example, vegetation algorithm can be configured toanalyze a given frame to partition any pixels having attributes of thecolor “green” form a Bayer filter. In another example, the algorithm canbe configured to detect attributes of “green” under any color modelwhere “green” is defined. For example, a numeric representation of RGBcolor being (r,g,b) where the value of g>0 in any digital number-bit perchannel. The algorithm can itself be a machine learning algorithm todetect “green” or a different color that are of interest.

In one example, machine learning and other various computer visionalgorithms can be configured to draw bounding boxes to label portions ofimages with objects of interest from backgrounds of images, maskingfunctions to separate background and regions of interest or objects ofinterest in a given image or portion of an image or between two imageswhere one image is a first frame and another image is a subsequent framecaptured by the same image sensor at different times, perform semanticsegmentation to all pixels or a region of pixels of an given image frameto classify each pixel as part of one or more different target objects,other objects of interest, or background and associate its specificlocation in space relative to the a component of the treatment systemand the vehicle supporting the treatment system.

Multiple techniques can be performed in layers to the same or portionsof the same image. For example, a computer vision technique or machinelearning technique can be first applied to an image to perform colorsegmentation. Once a given image is detected and pixels related to adesired or target color is segmented, the separate machine learningalgorithm or computer vision algorithm can be applied to the segmentedimage, for example to an object detection algorithm to draw boundingboxes around the segmented image containing weeds and containing crops.In another example, an object detection algorithm can be applied to theentire image to draw bounding boxes around plants of interest, such ascrops and weeds. Once the image has bounding box detections draw aroundeach of the detected crop or weed objects in the image, a colorsegmentation algorithm can be applied to just those bounding boxes toseparate pixels bounded by the box that are of a target color, such asgreen, and those pixels that are considered background. This method canallow a system to more accurately determine which pixels are associatedwith objects in the real world, such that an image with contours andoutlines of a specific object detected in the image, such as a leaf, canbe a more accurate depiction of the leaf, and therefore more accuratelytarget the leaf in the real world, than drawing a rectangular box arounda leaf where the system determines that any portion inside the boundedrectangular box is associated with the object “leaf”. The example aboveis just one of many examples, configurations, orders, layers, andalgorithms, that can be deployed to analyze a given image for betterunderstanding of objects, that is improved feature detection, performedeither online in the field in real time, or offline at a server forother uses, such as creating a time lapse visualization, mapping theobject, generating key frames with detections for indexing and storage,diagnosing and improving machine learning models, etc.

In one example, detecting a plurality of agricultural objects and/orlandmarks can be used to perform variations of consensus classification.For example, multiple detections of the same agricultural object and/orlandmark can be performed to eliminate or reduce false positives orfalse negatives of object detection. While a machine learning model willbe tasked to identify individual objects and landmarks, the closeness ofan object to another object in a single frame can also be accounted foran considered by the machine learning detector for detecting an object.For example, if in a first frame, the machine learning detector detectsa target object as well as a plurality of nearby target objects, otheragricultural objects, or landmarks, but then in subsequent frames, whilethe vehicle has not moved enough such that the location where the MLdetector has detected a target object has not moved out of the nextframe, does not detect that same target object, but does detect all ofthe other nearby target objects, other agricultural objects andlandmarks detected in the first frame, the compute unit can determinethat the first frame may have had a false positive and flag the framefor review and labelling, at a later time on board the vehicle for ahuman to label, or offline, without instructing the treatment unit toperform an action at the location in the real world where the systemdetected a target object to treat based on the first frame.

FIG. 16 illustrates an example diagram 1600 for ingesting an image,performing various computer vision and machine learning algorithms ontovarious portions or layers of the image to extract and detect featuresof the image.

As discussed above, multiple techniques can be performed in layers tothe same or portions of the same image. For example, an image 1610 canbe acquired by an image capture device and loaded onto a local computeunit of a local modular treatment module. For illustration purposesonly, the image 1610 captured can be an image of a row crop farm havingone or more beds 1612 supporting a plurality of crops, such as carrots,and weeds, and one or more furrows or tracks 1614 for a vehicle's wheelsto run through as a vehicle passes the row. One or more embedded machinelearning algorithms and computer vision algorithms in the compute unit,or accessible by the compute unit in real time via the cloud or edgecompute device containing the machine learning algorithm and computervision algorithm, such as computer vision algorithm 1620 and machinelearning algorithm 1630 can be used to partition the image 1610 intoanalyzed images with features extracted, with the goal of accuratelydetecting objects in the given image 1610. For example, the firstcomputer vision algorithm 1620 configured to separate beds and furrowscan be applied to the analyze and segment classify the image 1610 withportions of the image related to beds such as partitioned image 1613with portions of the image related to furrows such as partitionedbackground image 1615. One purpose of deploying this technique is tothat the treatment module does not have to run a machine learningdetector on the entire image 1610, but only on portions where object ofinterest may be. The partitioning of beds and furrows, as is thepartitioning of green and background, are just many examples ofperforming a plurality of computer vision and machine learningtechniques to an image to reduce computation load while generatingaccurate detections of features in the real world. Next, the system willhave generated a partitioned image 1616 having pixels associated withbeds and pixels associated with furrows such as that of partitionedimage 1613 and partitioned background image 1615. The machine learningalgorithm 1630, which for example can be a machine learning algorithm todetect plant objects of interest, such as crop plants and variousspecies of weeds, can be implemented to further analyze the image 1610or the partitioned image 1616, and only the portion of the image 1610that is partitioned image 1613, and not the partitioned image 1615. Thiswould allow the ML detector or machine learning algorithm 1630 toanalyze fewer pixels or tiles of pixels, and reduce the load on thesystem, while the system having a high probability that the machinelearning detector is scanning the most important areas of the image1610. In this example, the detector would run detections on only aportion of the partitioned image 1616, for example a portion of thepartitioned image 1613, such as a patch 1632 of the partitioned image1613. The treatment system can then draw bounding boxes, semanticallyclassify, or perform various machine learning methods deployed bymachine learning algorithm 1630, for example detect objects and drawbounding boxes, and generated a machine labelled or machine detectedimage 1642, which is a labeled of image of a portion of the originalintake image 1610. The agricultural observation and treatment system canthen use those detections to determine which detections are targetobjects to treat, target the objects in the real world, track thedetected objects in subsequent frames, and perform a treatment action tothe detected object in the real world. Additionally, using multiplelayers of computer vision and machine learning algorithms to optimizethe computing load onto a compute unit can be performed to improveVSLAM. For example, vegetation segmentation can be performed to detectgreen objects. In the VSLAM pipeline for matching keypoints from frameto subsequent frames by the same sensor, the compute module or computeunit associated with the sensors receiving the images, can determinethat points associated with green objects are real objects in the worldthat are stationary and can be tracked via VSLAM by sensors and computeunits of each component treatment module for local pose estimation. Thiswould allow the VSLAM algorithm analyze keypoints, keypoints in thiscase being points related to corners or contours or edges of greenobjects, with higher confidence that the keypoints generated andanalyzed are higher quality than that of arbitrary salient points,compared to that of known objects, such as objects corresponding togreen pixels, since the system will know beforehand that green pixelsare of vegetation, which are physical objects in space that arestationary and are of similar size and topography as that of targetobjects for treatment that will be tracked.

FIG. 17A illustrates an example method 1700 that may be performed bysome example systems or subsystems described in this disclosure eitheronline, that is onboard a vehicle supporting one or more modularagricultural observation and treatment systems, subsystems, orcomponents of systems, or offline, that is at one or more servers oredge compute devices.

At step 1710, the agricultural observation and treatment system caninitialize the treatment system. At step 1720, the agriculturalobservation and treatment system can obtain a first image having one ormore unique regions of interest. For example, the regions of interestcan be regions or portions of images that are specific to a specificgeographic boundary such as a row crop farm or an orchard. For example,images where there are tree trunks, images where a substantial portionof the image are either beds or troughs or furrows, images where objectsof interest have a certain color and every other portion of the imagecan be background. At step 1730, the agricultural observation andtreatment system can identify the one or more unique regions of interestand one or more regions of background. At step 1740, the agriculturalobservation and treatment system can partition the first image into theone or more unique regions of interest and the one or more regions ofbackground of the first image. At step 1750, the agriculturalobservation and treatment system can identify a first region of interestamong the regions of interest. At step 1760, the agriculturalobservation and treatment system can detect one or more objects in thefirst region of interest. At step 1770, the agricultural observation andtreatment system can the agricultural observation and treatment systemcan determine a real-world location of a first object of the one or moreobjects based on a location of the first object detected in the firstimage. At step 1780, the agricultural observation and treatment systemcan determine and prepare one or more actions associated with the firstobject in the real world. At step 1790, the agricultural observation andtreatment system can send instructions to activate actuators. The systemcan repeat steps 1760 to detect a second object detected in the firstregion and prepare treatment actions associated with the second object.Once all objects of interest are accounted for in the first region ofinterest, the system can detect objects in a second region of interestfor treatment, or partition the image for a second region of interest.

Additionally, at step 1782, the agricultural observation and treatmentsystem can identify a second region of interest. At step 1784, theagricultural observation and treatment system can detect one or moreobjects in the second region of interest. At step 1786, the agriculturalobservation and treatment system can determine a real-world location ofa second object based on a location of the second object detected in thesecond region of interest in the first image. At step 1788, theagricultural observation and treatment system can determine and prepareone or more actions associated with the second object.

FIG. 18A is a diagram 1800 capturing an action performed by anobservation and treatment system. In this example, an image capturedevice can receive a constant stream of images of a local scene havingone or more agricultural objects in the scene. Once a target object isdetected, targeted, and tracked, the system will instruct a treatmentunit to activate and emit a liquid projectile or a beam of light onto asurface of the target object. This action will take a length of time torelease from the treatment unit to exiting the treatment head, travel inspace, hit the target if accurately targeted and emission parameters,such as dwell time which is the amount of time the nozzle head ispointed at the target object while the nozzle head is on a movingvehicle, pressure release time which is when a pressure actuator such asa capacitor or solenoid valve opens and closes and allows pressurizedfluid to release from the valve and through the nozzle head, nozzleorifice size, and other parameters, and create splash, splat, or afootprint on the ground for row crops where plants, or target plants,are growing out from the ground.

In this example, the image capture system can capture and trace theliquid projectile itself, for example fluid projectile 1830. Because theprojectile is a fluid, it may not flow it an exact straight line.Additionally, the projectile can be comprised of smaller liquid droplets1850. The compute unit and image sensors can detect the beam tracedirectly from detecting the projectile 1830 and its smaller droplets1850 as the liquid leaves the treatment unit. Additionally, a laser witha laser beam 1840 can be pointed at the intended target object 1820 forthe system to detect both the laser beam and trace the projectile beamto determine whether there was a hit, and if there was any error ordiscrepancy form the desired projectile hit location to the actualtrajectory of the proj ectile.

FIG. 18B and FIG. 18C illustrate an example of spray detection, beamdetection, or spray projectile detection. In these diagrams 1802 and1803, one or more image sensors is scanning a local scene comprising aplurality of plants 1872 including target plants for treatment and cropplants for observation and indexing. As the sensor scans the scene whilea vehicle supporting the sensor is moving in a lateral direction, thesensor will capture one or more image frames in sequence from one toanother illustrated in image frames 1862, 1864, and 1866 where imageframe 1864 and 1866 are frames captured by a sensor that captured imageframe 1862 subsequently, but not necessarily the immediate next framecaptured by the image sensor. During the capturing of images, ifcomponent treatment system having sensors and treatment units sendsinstructions to the treatment unit to perform a spray action, such asemit a fluid projectile, the image sensors would capture the sprayaction as it comes into the frame and then eventually disappears as theprojectile is fully splashed onto the surface of the intended target orground. In such example, the spray projectile, such as projectile 1875,can be detected and indexed by the image sensors and the treatmentsystem, as well as the splat area 1877 after the spray has completed.The system can detect the splat size and location.

In one example, the detection of the spray can be performed by variouscomputer vision techniques including spray segmentation, colorsegmentation, object detection and segmentation, statistical analysisincluding line fitting, homography estimation, or estimation of ahomography matrix, or a combination thereof. For example, thedifferences between frame 1862 and frame 1864 can be the presence of aspray and the lack of presence of a spray. The rest being the samefeatures in each image. In one example, homography estimation is used toaccount for change in space across a common plane, such as a bed of arow crop farm. A homography matrix can be used to estimate how muchmovement in space from a first frame to a subsequent frame. The imageswill be slightly misaligned from each other due to the camera being on amoving vehicle while the first frame 1862 is captured and a subsequentframe 1864 is captured. The discrepancy in in the frames caused by themotion of the camera can be accounted for with homography estimation,given that the two frames are likely looking at the same plane of equaldistance from the camera from the first frame 1862 to the subsequentframe 1864, at a later time but not necessarily the exact next framecaptured by the image capture device. The difference in the two images,other than the discrepancy which can be accounted for by homographyestimation, would be the presence of the spray, which can be generatedby comparing the two frames and performing spray segmentation, that isthe pixels in frame 1864 that has the spray projectile 1875 capturedcompared to the pixels in frame 1862 that do not have a projectiledetected. In this case, one or more statistical and image analysistechniques, including line fitting, and masking function to determinethat the pixels detected in frame 1864 but not detected in frame 1862 isa spray projectile. Since spray projectiles are likely line shaped, thepixels related to the spray can be line fitted. Other image differentialtechniques can be applied to detect the spray beam including outlierrejection and using priors for masking outliers. The priors can be anexpected region such as that outline by predicted spray path 1876. Inone example, the difference in pixels profiles detected from a firstframe to a subsequent frame, accounting for homography estimation due tochanges in translation of the image sensor, can generate a projectilesegmentation. Similar techniques can be used to detect the splat or spotdetection of the spray outcome onto the surface of the target andground, for example, seeing the color of the ground and target plantchange from unsprayed to sprayed. For example, a liquid projectilehitting a target plant will morph from a projectile having a smallcross-sectional diameter to a flat area covering a portion of the dirtor leaf. In this example a liquid projectile may change the color of thedirt surrounding a plant, due to dry dirt turning wet from the liquidprojectile hitting the dirt. In this case, the image sensors can detecta color change in the ground and determine that a splat is detected andthat a detect target object for treatment has been treated, and loggedor indexed by the treatment system. In one example, a stereo pair ofcameras can detect sprays in each camera and associated with each otherto fit a 3D line such that the system can detect and index a spray inthe real world with 3D coordinates.

FIG. 18C. illustrates a diagram 1803 to determine spray accuracy andspray health, spray health being whether external factors outside orcorrectly detecting target object and lining the treatment head onto thetarget object and tracking it as the target object moves away from thetreatment unit, since the treatment unit is on a moving vehicle, a prioror predicted spray path 1876 can be generated. For example, a sensor,disposed on a moving vehicle, can receive an image frame 1862 having aplurality of crop objects and target objects, including detected targetobject 1872. The treatment system will target the target object 1872,track the object 1872 in subsequent frames, such as that of frame 1862,and emit a projectile onto target 1872. In one example, due to externalfactors not necessarily related to computer vision, such as portions ofthe treatment unit no longer calibrated to the image sensor, such thattargeting at a specific location in the real world from a detection inthe image frame may result in a misalignment of the line of sight of thetreatment head. For example, the treatment system, given frame 1862 or1862, may target the target object 1872 at the correct real-worldlocation, but in doing so and instructing the treatment head to aim itsnozzle to target object 1872 in the real world may in fact be targetinga location 1879 or 1878, or an incorrect location or misaligned locationin the real world, that the treatment systems image sensor wouldcapture. In this case, to quality check the spray targeting and sprayaction, the treatment system can predetermine a predicted spray path1876 and perform spray segmentation and other computer vision andmachine learning techniques described above only in the portion of theimage, and therefore compare pixels related to the images contained inthe region defined by the predicted spray path 1876. If the detection isnot good enough, such as the line cannot be fitted, the system candetermine that the spray did not happen, or happened but not at theintended target. Alternatively, the system can perform spraysegmentation on the spray that was detected, whether within thepredicted spray path 1876 or not, and determine whether the end of thespray or the splat detected lines up with the intended target. Thus,seeing where a target object should have been sprayed, and/or shouldhave had a splat detected, and where the actual spray profile wasdetected, including 3D location, and where the spray splat was detected,can be used to evaluate the specific spray health of that particularspray, and whether intrinsic or extrinsic adjustments needs to be made.The adjustments can be accounting for wind that may have moved thespray, the speed of the vehicle not being accounted for properly as thesystem tracks an object from frame to frame, or mechanical defects suchthat the intended target and the line of sight after sending the correctinstructions to orient the treatment head of the treatment unit aremisaligned. Upon detecting an inaccurate or incorrect spray projectile,one or more of the discussed defects can be accounted for in real timeand a second projectile can be reapplied on to the target object andtracked again for trajectory evaluation and its spray health andaccuracy.

FIG. 18C illustrates an example method 1804 that may be performed bysome example systems or subsystems described in this disclosure eitheronline, that is onboard a vehicle supporting one or more modularagricultural observation and treatment systems, subsystems, orcomponents of systems, or offline, that is at one or more servers oredge compute devices.

For example, at step 1806, the observation and treatment system orserver can identify a first object for treatment. In this example, theobservation and treatment system or a server is analyzing theperformance of the online observation and treatment system during itslatest run, in a location such as an agricultural geographic boundary.The system, online or at a server, can identify each treatment performedor instructed to be performed on the geographic boundary forverification, indexing, and adding the verification to each of theidentified target object's treatment history. For example, a treatmentsystem may have identified and initialized a few thousand or a fewhundred thousand actions performed in a single run at a field, orchard,or farm, and a server is analyzing the treatment accuracy and efficacyof each of the actions performed on the field in that particular run. Atstep 1808, the observation and treatment system or server can determinea treatment unit activation for each of the objects for treatment. Inthis optional step, the system or server can determine treatment actionsbased on the treatment performed and logged previously in real timewhile the observation and treatment system was on the field performingdetection objects and performing treatments. In this example, the serverdoes not have to identify every frame captured and determine whichobjects detected were treated for second time, but instead can analyzeonly those frames captured by image capture devices where each onlineand onboard compute unit has already detected. In one example, thedetermining of treatment activation can include the treatment parameterssuch as desired spray size, volume, concentration, mixture of spraycontent, spray time of flight, etc.

At step 1812, the observation and treatment system or server can detecta first emission pattern. This can be done with techniques describedabove as well as image correspondence from a previous frame and asubsequent frame to detect a projectile.

At step 1813, the observation and treatment system or server can indexthe first emission pattern. This can be stored as a 3D vector, or a 2Dor 3D model of the full 3D profile with shape and orientation mappedinto a virtual scene.

At step 1814, the observation and treatment system or server can detecta first treatment pattern. This can be the splat detection from colorchange in dirt from a first frame to a subsequent frame, performed bysimilar methods described above.

At step 1815, the observation and treatment system or server can indexthe first treatment pattern.

At step 1816, the observation and treatment system or server candetermine and index the first object as treated. For visualizationpurposes, a target object that has not been accurately treated can havea bounding box with a dotted line indicating a detection of the objectitself but no detection of a spray onto that target object. And once aspray or treatment is detected, by the projectile or the splatdetection, the dotted line can convert to a solid line, as illustratedin diagram 1803 of FIG. 18C.

As illustrated in FIGS. 18E and 18F, each spray projectile and splatdetections can be indexed and visually displayed in a user interface.The 2D or 3D models 1880 a, 1880 b, and 1880 c of each target object1872, spray projectile 1875, and splash 1877 onto a surface of theground and target object. Additionally, the 3D models can besuperimposed on each other to reconstruct the spray action from thetargeting of the target object, to the spraying of the target object, tothe splash made and splat detected as illustrated in model 1880 d ofdiagram 1806 of superimposed model 1882.

FIGS. 19A, 19B, and 19C illustrate diagram 1900 depicting and exampletreatment pattern performed and observed for treatment accuracy andhealth. For example, as illustrated in FIG. 19A, a treatment module 1930with a treatment unit configured to apply different chemical solutionsor water, and different treatment profiles, such as a projectile or avolumetric scan of fluid over a region on a field or tree, having one ormore sensors, can be configured to sense objects 1904, determinetreatment parameters, and perform treatment actions illustrated bytreatment 1940 and treatment projectile 1932. In this example, treatmentprofile 1940 is a blasting spray of fluid covering a sweep angle 1922and treating a plurality of objects 1904 over a ground 1902.Alternatively, treatment 1932 is a fluid projectile treatment onto asingle object 1904. The treatment module 1951 can line a treatment unitwith a line-of-sight 1924 with a normal angle 1928 from a vertical path1926, onto the object 1904. Additionally, as illustrated in FIG. 19B,treatment module 1951 can also be configured to emit small fluiddroplets 1934 or larger fluid droplets 1936 onto objects 1904. The aboveillustrations are merely examples as the treatment units can beconfigured to emit a plurality of different types of fluid treatmentsdiscussed in this disclosure. FIG. 19C illustrates examples of certainintrinsic or extrinsic factors affecting the desired spray profile orspray accuracy of spray treatments. Of example, with chemical buildup ina spray nozzle or tubing, particularly salt-based substance orsurfactant built up in the inner walls of a spray nozzle, can affect avolumetric spray profile illustrated in treatment profile 1940 to thatof treatment profile 1942, which may not be the desired treatmentprofile. Additionally, other extrinsic factors such as wind can affectspray projectiles such that even when the target is accurate and theline of sight of the treatment nozzle is correctly aligned with thetarget object, the wind can affect the trajectory of the fluidprojectile emitted from the treatment nozzle such as projectiletrajectory or path 1962 of fluid projectile 1908. Or even without wind,of substance is built up in the inner walls of tubing or the treatmenthead nozzle, even when the line-of-sight 1924 is correct, and theprojectile shoots straight out of the nozzle undisturbed by wind, thetrajectory can be off from the desired trajectory, such as trajectory1964.

FIG. 20 illustrates an example method 2000 that may be performed by someexample systems or subsystems described in this disclosure eitheronline, that is onboard a vehicle supporting one or more modularagricultural observation and treatment systems, subsystems, orcomponents of systems, or offline, that is at one or more servers oredge compute devices.

For example, at step 2010, an agricultural observation and treatmentsystem can identify an object for treatment. Multiple objects can beidentified from a single image frame and each tracked after the systemstargets the objects. At step 2020, the agricultural observation andtreatment system can lock a treatment unit onto a target object. At step2030, the agricultural observation and treatment system can activate atreatment unit. At step 2040, the agricultural observation and treatmentsystem can detect an emission target error. At step 2050, theagricultural observation and treatment system can detect a treatmentunit health. At step 2060, the agricultural observation and treatmentsystem can detect one or more internal drift factors, one or moreexternal drift factors, or both. At step 2070, the agriculturalobservation and treatment system can determine a target accuracyadjustment. At step 2080, the agricultural observation and treatmentsystem can realign the treatment unit onto the target object accountingfor one or more post-emission error. At step 2090, the agriculturalobservation and treatment system can active treatment unit to treat thetarget object found in step 2020.

FIGS. 21A-24F illustrate various examples of performing agriculturalobservation, digitizing a geographic boundary, building a map of eachindividual agricultural object or crop detected and associating capturedimages of agricultural objects from one moment in time to another todigitize and map a farm with location and image history of eachagricultural object detected, targeting and tracking objects, andtreating each individual agricultural object.

The description of buds, blooms, flowers, fruitlets, and otheragricultural objects and stages of growth of such agricultural objectsdiscussed are only meant to be an example series of objects that can bedetected by a treatment system, detecting fruits and objects associatedwith the stages of growth of fruits on fruit trees, and not meant to belimiting only to the specific example described above. For example,agricultural objects can include larger objects or portions of a treethat are part of supporting a crop can be detected, classified, andlabelled for spraying including spurs, shoots, stems, laterals, othernodes, fruiting clusters, leaves, or other portions of a tree. Differenttypes of plants can be treated by the treatment system including generalplants for crops, specialty crops, including fruits, vegetables, nuts,flowers, herbs, foliage, etc. The agricultural treatment systemsdescribed in this disclosure can be performed in geographic boundariestypically appropriate for a robotic vision and treatment depositionsystem for observing, treating, harvesting, or a combination thereof, ofcrops such as farms, orchards, greenhouses, nurseries, or otherregionally and topographically bounded locations for agronomy andagriculture, horticulture, floriculture, hydroculture, hydroponics,aquaponics, aeroponics, soil science and soil agronomy, pedology, etc.

FIGS. 21A and 21B are side and front views, respectively, of theillustration depicted in FIG. 6 . The vehicle can include spatial andnavigation sensors, for localizing the vehicle and objects, as describedabove. The sensors 2116 can be visual odometry and VSLAM sensorsincluding various types of cameras, rangefinders, and LiDAR. Eachmodular treatment system supported by the vehicle 2110 can includetreatment units 2112 for emitting a treatment projectile or droplet ontoa treatment target 2132, including agricultural objects of interest.

FIG. 21C illustrates a vehicle supporting or towing one or moretreatment systems 2112 configured and optimized for a geographicenvironment having plants grown on V-shaped trellises 2134 or grown in a“V” shape.

FIG. 22 is a diagram illustrating the vehicle 2110 supporting thetreatment system 2112 in an alternative orientation.

FIG. 23 illustrates a vehicle having coordinates associated withrotational movement including that of roll about an X axis, pitch abouta Y axis, and yaw about a Z axis, as well as translational coordinatesassociated with lateral movement including an X, Y, and Z position in ageographic boundary. The vehicle 2110, illustrated in FIG. 23 can movewith at least 6 degrees of freedom. Additionally, the treatment unit2113 of the treatment system 2112 can also have coordinates associatedwith rotational movement including that of roll about an X axis, pitchabout a Y axis, and yaw about a Z axis, as well as translationalcoordinates associated with lateral movement including an X, Y, and Zposition in a geographic boundary. This can include rotating and movinga gimbal assembly of the treatment unit 1653 to a desired pitch angle2002 and desired yaw angle 2004 when the treatment unit is configuringand orienting itself to position a nozzle or head of the treatment unit1653 at a target or aligning a line of sight towards a target foremitting a projectile.

FIG. 24A illustrates a diagram 2400 including a vehicle 2410, having oneor more sensors 2418 and other electronic devices, supporting and towingone or more treatments systems 2412. In one example, the vehicle 2410can be a tractor towing a plurality of modular treatment systems 2412.FIG. 24B illustrates the diagram 2402 with an alternate orientation ofthe treatment systems 2412 being towed by vehicle 2410.

Further illustrated in FIG. 24C, a vehicle 2410, such as a tractor isconfigured to tow one or more treatment systems 2412 along a vehicletrack 2430 having multiple lanes for the vehicle to operate a geographicboundary. Between each vehicle track 2430 are one or more rows ofagricultural objects 2432, such as plant including crop plants and weedplants, for each treatment system 2412 to scan across each row toobserve and treat individual plants growing form the ground.

As illustrated in FIG. 24D, the treatment systems 2412 can be configuredto observe a plant, soil, agricultural environment, treat a plant, soil,agricultural environment, or a combination thereof, such as treating aplant for growth, fertilizing, pollinating, protecting and treating itshealth, thinning, harvesting, or treating a plant for the removal ofunwanted plants or organisms, or stopping growth on certain identifiedplants or portions of a plant, or a combination thereof.

In one example, the treatment systems can be configured to observe andtreat soil for soil sampling and mapping of features and chemicalcompositions of soil including soil deposition, seed deposition, orfertilizer deposition, nutrient management, for both cultivated anduncultivated soil. The agricultural objects described above fortargeting and treating can be of specific patches of soil that can beidentified and features and classification labelled by a vision of thetreatment system. Each patch or region of the soil detected by thetreatment system 2412 and can be indexed and mapped with a timestampassociated with the moment the patch or region was sensed and treatmenthistory detailing each treatment applied to each patch or region of thesoil.

FIG. 24E illustrates a diagram 2408 depicting an example treatmentsystem having a plurality of component treatment modules 2444 supportedby a support member 2440 and a navigation unit 2442 (various sensor ofthe navigation unit 2442 may not necessarily be enclosed in a boxillustrated in diagram 2408).

In one example, each component treatment module 2444, via its owncompute unit and image sensors, and other sensors, can perform VSLAM tocontinuously map a local environment in the agricultural scene andcontinuously generate pose estimation, such as a local pose estimationrelative to objects or landmarks detected on the ground, such as plantobjects, patterns, salient points representing unknown objects near theground, including target plant objects. Additionally, the navigationunit 2442, via its own compute unit and image sensors, GPS, IMU, andother sensors, can perform VSLAM and VIO to continuously map a globalscene and continuously generate a pose estimation, such as a global poseestimation of a global scene. The compute unit of each treatment module2444, can account for both its locally determined pose estimationrelative to objects and landmarks on the ground, and the globallydetermined pose estimation of a global scene relative to a point oforigin in an agricultural environment, such as a farm, because each ofthe treatment modules 2444 a, 2444 b, 2444 c, etc. are rigidly attachedto a support structure supported by a vehicle having sensors locatedthroughout the vehicle associated with the navigation unit such thattranslation of the vehicle, which includes a change in global poseestimation detected, will have substantially the same translation ofeach of the component treatment module, and therefore also includes achange in a global pose estimate detected and accounted for eachcomponent treatment module.

In one example, tracking multiple poses of each component treatmentsystem, being the local pose generated from local sensors to thecomponent treatment system, and global pose received form the navigationunit, can account for loss and/or inaccuracies of kinetic motion fromthe vehicle to each of the component treatment systems, particularly thecomponent treatment systems that are farther away from the vehicleitself, relative to modules supported by the vehicle that are closer tothe vehicle. This is especially apparent in farming activities whereperformance of any agricultural observation and treatment system willlikely be performed on rough topography such that movement along a pathwill cause various magnitudes in bumps, and thus change in height, alongthe path. For example, as a vehicle navigates in a rough terrain, thecomponent treatment module 2444 c will likely bump up and down moreviolently than the bumping of component treatment module 2444 a. Thus,it may be impractical for each component treatment system to onlydetermine its local pose estimation from that of the global poseestimation as movement of sensors of the navigation unit 2442, or thatof the navigation unit 2442 box itself, will be different from themovement of, for example, the component treatment module 2444 c. It thiscase, each of the component treatment modules 2444 can determine itslocal pose to more accurately detect and track targets in real time fortreatment actions.

In one example, when a compute unit of the first treatment module 2444sends instructions to each of one or more treatment units, for exampletreatment devices with one or more nozzles on a turret or gimbalmechanism, the agricultural treatment system can determine the specificpose of each of the nozzle heads at the time the treatment module,through local or global poses, global being vehicle and local being ator near each treatment module 2444, detect and identify an object andits location relative to the treatment module, as well as determine thelocation of the object in the global scene. At this point, the computeunit of the first treatment module can account for the vehicle's pose ofthe global scene, that is the global registry of a farm, the pose of thetreatment module itself relative to the local first target object, aswell as account for the last state of orientation of the treatmentunit's nozzle or emitter's line of sight relative to the treatmentmodule. This is because the vehicle, the treatment module, morespecifically the treatment module's local sensors, and the treatmentunit are all mechanically coupled in a fixed position to each other.Thus, a change in pose estimation generated by sensor signals of thevehicle itself will directly translate to the same change in poseestimation to anything physically supported by the vehicle. However,calculating for pose at each treatment module as well as accounting forpose of the vehicle, with sensors and computer vision techniques such asperforming visual SLAM using machine learning to detect objects totrack, particularly for treatment modules that are disposed farther awayfrom the vehicle as compared to other treatment modules closer to thevehicle, and therefore the sensors of the navigation unit 2442.

In one example, multiple rows where each treatment module 2444 candetermine a pose estimation based on determine its own pose local posewith its local sensors embedded or supported by the module 2444, andthat of the vehicle's pose. Thus, each object identified, can be indexedin the real world such that if the vehicle operates on the samegeographic area in a subsequent day, or any subsequent time where abreak in operation has occurred, an object detected in the subsequenttime can be matched and associated with an object previously identifiedsince in at least both cases, the treatment system determined thelocation of each object identified in the real world, global scene, byapproximating its location in the real world with the treatment system'ssensed and determined global map of the geographic boundary and furthernarrowing down its local position relative to a point in the global mapof the geographic boundary to a specific point in the geographicboundary with each of the treatment module's sensed and determined localposition of the object relative to the vehicle and/or treatment module.

FIG. 24F illustrates an example method 2450 that may be performed bysome example systems or subsystems described in this disclosure eitheronline, that is onboard a vehicle supporting one or more modularagricultural observation and treatment systems, subsystems, orcomponents of systems, or offline, that is at one or more servers oredge compute devices. For example, at step 2452, an agriculturalobservation and treatment system can determine a first vehicle poseestimation. At step 2454, the agricultural observation and treatmentsystem can determine a first treatment module pose estimation. At step2456, the agricultural observation and treatment system can determine afirst orientation of a treatment unit. At step 2458, the agriculturalobservation and treatment system can determine a first pose estimationof the first treatment unit. This is done by accounting for the poseestimation of the first treatment module operably and rigidly connectedto the treatment unit and knowing the prior orientation, such as thefirst orientation, of a treatment head of the treatment unit, such asthe orientation of the treatment head when it last sprayed a projectile.At step 2460, the agricultural observation and treatment system candetermine a location of a first target object.

FIGS. 25A-25E illustrate example agricultural treatment units, such astreatment unit 2500 of an agricultural treatment system. Theagricultural treatment system in this example, can be similar to that ofagricultural treatment system 400 with treatment unit 470 similar tothat of treatment unit 2500. Additionally, the treatment system havingtreatment unit 2500 can be similar to that of agricultural treatmentsystem 800 with treatment module 804 and treatment unit 828, includingmultiple agricultural treatment module 804′s and treatment unit 828′soperably connect together in agricultural treatment system 800. In thisexample, the treatment unit 2500 can include a turret assembly with atreatment head 2560, and circuitry, electronic components and computingdevices, such as one or more microcontrollers, electronic control units,FPGA, ASIC, system on chip, or other computing devices, configured toreceive instructions to point and orient the treatment head 2560, totreat a surface of a real-world object in proximity of the treatmentunit 2500. For example, the treatment unit 2500 can emit a fluidprojectile of a treatment chemical onto an agricultural object in thereal world based on detecting the agricultural object in an imagecaptured and determining its location in the real world relative to thetreatment unit 2500.

The treatment unit 2500 can include a gimbal assembly (including linkagecomponents 2542, 2544 and 2550), such that the treatment head 2560 canbe embedded in, or supported by the gimbal assembly, effectivelyallowing the treatment head 2560 to rotate itself and orient itselfabout one or more rotational axes. For example, the gimbal assembly canhave a first gimbal axis, and a second gimbal axis, the first gimbalaxis allowing the gimbal to rotate about a yaw axis, and the secondgimbal axis allowing the gimbal to rotate about a pitch axis. In thisexample, a control module of the treatment unit can control the gimbalassembly which changes the rotation of the gimbal assembly about itsfirst gimbal axis, second gimbal axis, or both. A computing module candetermine a location on the ground scene, terrain, or tree in anorchard, or other agricultural environment, and instruct the controlmodule of the treatment unit 2500 to rotate and orient the gimbalassembly of the treatment unit 2500. In one example, the computingmodule can determine a position and orientation for the gimbal assemblyto position and orient the treatment head 2560 in real time and makeadjustments in the position and orientation of the treatment head 2560as the treatment unit 2500 is moving relative to any target plants oragricultural objects of interest on the ground either in a fixedposition on the ground or is also moving. The treatment system can lockthe treatment unit 2500, at the treatment head 2560, onto the targetplant, or other agricultural object of interest through instructionsreceived and controls performed by the control module of the treatmentunit 2500, to adjust the gimbal assembly to move, or keep and adjust, inreal time, the line of sight of the treatment head 2560 onto the targetplant.

In one example, the treatment unit may have a frame 2510 to which thebrushless motors 2520, 2530 are attached. The frame 2510 may be attachedor secured to other structures, housings or other frames. An encoder2520 may be coupled with brushless motor 2520. An encoder 2532 may becoupled with brushless motor 2530. The encoders 2520, 2530 may be usedby the treatment system to determine or identify a rotational positionof the brushless motors 2520, 2530.

Brushless motor 2520 is coupled to linkage bracket 2542 (may be referredto as a servo horn). The linkage bracket 2542 rotates axially in arotational axis of brushless motor 2520. Linkage bracket 2542 isrotatably coupled to linkage arm 2544. Linkage arm 2544 is rotatablycoupled to spraying head assembly (also referred to herein as atreatment head assembly) 2560. The spraying head assembly 2560 iscoupled to the linkage bracket 2544 at one location in a matter allowingthe spraying head assembly to pivot in a first axis. The coupling of thelinkage arm 2544 to the spraying head assembly allows rotation of thelinkage arm 2544 while the linkage arm 2544 causes the spraying headassembly 2560 to move to adjust the spraying head 2562 assembly 2560position. The spraying head assembly 2560 may be more generally referredto as a treatment head assembly. The treatment head assembly, forexample, may be configured as a spraying head assembly to emit a fluidat a target object and/or as a laser head assembly to emit a laser lightsource at a target object.

In one example, one or more brushless motors 2520, 2530 can beconfigured to change one axes of the treatment head 2560 of thetreatment unit. Brushless motor 2530 is coupled to linkage bracket 2550(may be referred to as a servo horn). The linkage backet 2550 rotatesaxially in a rotational axis of brushless motor 2530. Linkage bracket2550 is coupled to spraying head assembly 2560. The spraying headassembly 2560 is rotatably coupled to the linkage bracket 2550 at twolocations in a manner allowing the spraying head assembly to pivot in asecond axis within the linkage bracket 2550. The coupling of the linkagebracket 2550 to the spraying head assembly 2560 allows rotation of thespraying head assembly 2560, thereby moving or adjusting the sprayinghead assembly position.

The frame 2510, linkage brackets 2542, 2550, linkage arm 2544, sprayinghead assembly 2560 may formed from various materials, such as CNC'd,milled aluminum, cast or molded metals, cast or molded plastics,three-dimensionally printed parts, and any other suitable constructionor manufacturing method.

Each of the linkage bracket 2542, 2550 may have legs that extend fromthe bracket and interact with bracket stops. For example, FIG. 25Dillustrates linkage bracket 2542 have two legs 2542A, 2542B. A bracketstop 2514 (such as an elongated shaft, post or stud) is attached to theframe 2510. As the brushless motor 2520 causes the linkage bracket 2542to rotate in one direction or the other, the linkage bracket legs 2542A,2542B may touch upon the bracket stop 2514. The bracket stop 2514prevents the brushless motor 2520 from rotating further in a particulardirection.

The linkage bracket 2542, 2550 may be configured with different degreesof rotational movement and/or the degree of rotational movement. Forexample, linkage bracket 2542 may have a total degree of rotation of 120degrees, while linkage bracket 2550 may have a total degree of rotationof 100 degrees. The degree of rotation of either linkage bracket 2542,2550 is based on the distance between the inner portions of the two legsof the bracket. The degree of rotation may also be referred to as asweep angle. For example, the sweep angle of each motor may be about 50degrees on each axis. A shorter distance between the two legs wouldprovide a smaller range of rotation, as compared to a longer distancebetween the two legs which would provide a greater range of rotation.The system may rotate a motor from one end of the sweep angle to anotherend of the sweep angle very quickly, such as within micro ormilliseconds.

In one example, FIG. 25E illustrates a solenoid valve 2570 (e.g., afluid flow regulator) can be configured to open or close the valve whereconstant pressurized fluid from the pump is pumping fluid into thesolenoid valve 2570. The treatment unit can emit a projectile by anamount and time the valve is moved from a closed position to an openedposition. Also, the rotational axis of motors 2520, 2530 is indicated bythe dashed arrows on the respective motors.

While FIG. 25E illustrates a single solenoid valve 2570, the treatmentunit may be configured with multiple solenoid valves that areinterconnected with different pumps to obtain fluid from different fluidsources. For example, the agricultural treatment system may have foursource tanks to hold different types of fluids. Four pumps may beutilized, each of which can be fluidly interconnected with a respectivetank and a separate solenoid valve. The pump(s) may pressurize thefluid, for example in a range of 5-250 PSI. For example, when treatingan agricultural object such as carrots, the solenoid may control fluidpressure to about 40 PSI. When treating an agricultural object such asapples, the solenoid may control fluid pressure to about 80-100 PSI. Thesystem may be configured to allow two or more fluid source to be emittedat the same time. For example, the spraying nozzle of the spraying headmay have multiple spraying ports. Also, the fluid lines may be connectedbefore or after the solenoid valve to mix different fluid sources fromthe tanks. Pressure sensors may be connected along any part of the fluidchannels to determine the pressure of a line. The system may thenincrease or decrease the fluid pressure to a desired pressure viaopening/closing of the solenoid valve, and by adjusting the pumping ofthe fluid via a pump. The size and volume of each spray emitted by thespraying head may be adjusted by the system based on the targe object tobe sprayed. For example, the system may spray 1.25 mL at 80 PSI for oneagricultural object, and at a different volume and PSI for anotheragricultural object. Moreover, the spray radius may be determined as adistance from the spray nozzle to the targeted object. For example, thespray radius may be about 1-2″ diameter with a coverage at about 1 meterwith a +/−60 degree sweep at 0.094 m. Additionally, the spray time maybe adjustable and intermittent spray burst of fluid may be controlled bythe system.

In one example, the spraying head assembly 2560 has a detachablespraying tip 2562. One or more fluid carrying tubes may be connected tothe spraying head assembly 2560 for carrying fluid from one or moresolenoids to be emitted via the spraying tip 2562. The spraying tip 252may have a threaded base portion allowing the spraying tip 2562 to beattached to spraying head assembly. In some, configurations the sprayingtip 2562 has multiple ports that are connected to different fluid lines.This allows the spraying head assembly to emit fluid pumped fromdifferent tanks or fluid sources. For example, the spraying tip 2562 mayhave one, two, three or four ports in the spraying tip. Also, multipleports of the spraying tip may be connected to the same fluid source. Forexample, the spraying tip 2562 may have two ports. The two ports may beconfigured such that fluid emitted at target object would spray thetarget object at two different locations. The spraying tip can bevarious types of tips, including for example a hypodermic needle withone or more input ports or orifices for different types of fluids, andwith one or more output ports or orifices.

In one example, the system may determine a target object to be of aparticular type. Based on the particular type of the target object, thesystem may choose to treat the target object with a fluid source fromone or more tanks. For example, the system may determine the targetobject to be a weed and then cause fluid from a first tank to be emittedfor weed treatment. In another instance, the system may determine thetarget object to be a plant and then cause fluid (e.g., liquidfertilizer) from a second tank to be emitted at the target object.

This treatment unit is capable of emitting a projectile fluid at atarget object in a continuous manner over a period of time, such as manyseconds or minutes. The treatment unit may also emit short intermittentbursts of a projectile fluid at a target object. The spraying tip of thespraying head assembly may be configured to emit the projectile fluid ina stream-like manner where the fluid is kept together in a stream toimpact a target object in a focused area. The spraying tip of thespraying head assembly may be configured to emit the projectile fluid ina spray-like manner where the fluid is fanned out or the fluid separatedto impact a target object in a general area. The spraying head assemblymay be configured with one or more spraying tips and/or spraying portsthat allow for the emission of either type of fluid spray types. Thespraying of fluid from the spraying tip is variable and adjustable basedon the target object. In one example, for spraying carrots the distancefrom the spraying tip to the target object may be about 27 inches. Inanother example, for spraying apples the distance from the spraying tipto the target about may be about 0.5-1.5 meters.

The agricultural treatment system may use a controller to interact with,obtain sensor data and control the motors and the solenoids. In oneembodiment, to instruct the spraying head to a determined spraying headpose, the system instructs the motors to rotate in one direction or theother. The system may instruct the motors to rotate at a constant speedor a variable speed. The system may monitor the axial position of amotor via the coupled encoder to the motor. For example, as the motorrotates the encoder may provide a feedback signal or value to the systemthat identifies the distance that the motor has rotate. The sample rateof the encoders may be set to a predetermined sample rate or may be avariable sample rate. For example, the system an encoder may be set toobtain readings every 5 milliseconds. For example, the encoders mayoperate at various frequencies. In one example, the encoders pollinformation of the motors at about 8 KHz, while sending data to amicrocontroller at about 200 Hz. Alternatively, the system may vary thesample rate based on the rotation speed of the motor, the movement speedof the vehicle, or based on some other value or data obtained by thesystem. When the system determines that the spraying head is moved to adesired spraying head pose, then the system may instruct the solenoid toopen and emit a fluid at a target object. The system may then instructthe solenoid to close thereby stopping the fluid from being emitted fromthe spraying head.

In one embodiment, the system may instruct the motor to rotate in onedirection, and via feedback from the encoder, the system may determinethat the motor has rotated to a particular position or distance, andthen stop or cease instructing the motor to rotate. As described herein,when the motor rotates, the coupled linkage assemblies cause thespraying head to rotate or pivot in a particular direction. For example,the system may be configured that when a first motor rotates in a firstdirection, the spraying head would pivot or move along a first axis in afirst direction, and when the first motor rotates in an opposite seconddirection, the spraying head would pivot or move along the first axis ina second direction opposite to the first direction. Moreover, the systemmay be configured that when a second motor rotates in a first direction,the spraying head would pivot or move along a second axis in a firstdirection, and when the second motor rotates in an opposite seconddirection, the spraying head would pivot or move along the second axisin a second direction opposite to the first direction.

The motors may be configured so that the plane of rotation of each motorare substantially perpendicular to each other. For example, the plane ofrotation of each motor may be aligned from 75 to 105 degrees of eachother. In one configuration, the plane of rotation of each motor is 90degrees to one another.

In one example, the motors may be configured such that the center axisof each motor is aligned with a first and second rotational axis of thespraying head assembly. In a neutral or home position, the spraying headassembly may be positioned such that the spraying head assembly ispositioned at an x, y coordinate of 0,0. In this position, a line fromthe center axis of each motor would extend through the respectiverotational axis of the spraying head assembly.

FIG. 26A illustrates example implementations of method 2600 that may beperformed by some example systems described above including system 100,agricultural treatment system 400, system 600, and system 800. Forexample, in one mode of operation, at step 2610, the agriculturaltreatment system provides a spraying apparatus having two motors withlinkage assemblies that are used to control the movement of a sprayinghead. The spraying apparatus, for example, may be the treatment unit470, 1653, 1400, 2500 as described herein.

At step 2612, the agricultural treatment system 400 determines a firsttarget object to be sprayed with a fluid emitted from the spraying head.As described herein, the system 400 may determine a location and/orposition of the target object in the external environment.

At step 2614, the agricultural treatment system adjusts a position ofthe spraying head via rotation of the first and/or second motors. Asdescribed herein, the system 400 may provide instructions to the motorsof the treatment units to position the spraying head such that thespraying head may emit a fluid that would spray from the spraying headonto the target object.

At step 2616, the agricultural treatment system regulates a flow of thefluid obtained from a fluid source by controlling a regulator to openand close causing a flow of the fluid to start and stop. For example,the system 400, may regulate the flow and overall pressure of the fluidby opening and closing a valve to release pressurized fluid from theregulator through the spraying head. One or more fluid pumps may be usedto pump fluid from a fluid source and pressurize the fluid.

At step 2618, the agricultural treatment system emits the fluid,obtained from the fluid source, via the spraying head at the firsttarget object. The fluid is emitted from the spraying head for apredetermined amount of time. The system 100 may release the fluid in acontinuous stream or in intermittent bursts of fluid emitted towards thetarget object.

At step 2620, the agricultural treatment system maintains the sprayingof the fluid at the first target object while a vehicle moves along apath, where the spraying apparatus is connected to the vehicle. Asdiscussed herein, the system 400 may determine a pose of the vehiclewhile the vehicle is moving along a path. The system 400 translates thepose of the vehicle to commands to instruct movement of the sprayinghead such that the spraying head is “locked onto” the target object. Inother words, as the vehicle moves along a path the system determinesadjustments that are to be made so that the spraying head moves in a waythat the emitted fluid continues to spray towards the target object. Thesystem 400 calculates the position of where the spraying head should beoriented in relation to the changing pose of the vehicle. The system 400provides causes the motors to rotate as needed to adjust the sprayinghead.

FIG. 26B illustrates example implementations of method 2650 that may beperformed by some example systems described above including system 100,agricultural treatment system 400, system 600, and system 800. Thesystem may controllers and encoders to control the position of themotors, which in turn control the position of the spraying headassembly.

At step 2660, the agricultural treatment system determines a location ofa first target object. For example, the system identifies a targetobject in a 3-dimensional space to be treated. At step 2662, the systemdetermines a position to move a spraying head assembly to emit a fluidat the first target object. For example, the system may determine aspraying assembly pose to orient a spraying head of the sprayingassembly such that the spraying head may emit a projective fluid at thetarget object. At steps 2664, 2666, the system instructs the motors torotate in one direction or another. The motors may be instructed to bemoved simultaneously or one after another. The system instructs a firstmotor to rotate in a rotational direction, thereby moving the sprayinghead assembly in a first direction. For example, via interconnectedlinkages and/or gimbal structures the rotation of the first motor causesthe spraying head assembly to rotate or pivot along a first axis. Thesystem instructs a second motor to rotate in a rotational direction,thereby moving a spraying head assembly in a second direction. Forexample, via interconnected linkages and/or gimbal structures therotation of the second motor causes the spraying head to rotate or pivotalong a second axis.

While the motors are instructed to rotate, encoders coupled to themotors may read rotational positions of the motors. The encoders mayoperate simultaneously and read rotational values of the motors. At step2668, the system determines a plurality of first encoder output values,where the first encoder is configured to identify rotational positionsof the first motor. At step 2670, the system determines a plurality ofsecond encoder output values, the where the second encoder is configuredto identify rotational positions of the second motor.

Lastly, the agricultural treatment system stops rotation of the motorswhen the respective motors have moved the spraying head assembly into adesired position such that the target object may be treated with a fluidand/or laser light source. At step 2672, the system stops rotation ofthe first and second motors when the first and second encoder outputvalues are equal to a location of a desired or position of the sprayinghead assembly. In some instances, the first or second motors may stoprotating when the spraying head assembly has reached a respective x/yposition. When the spraying head assembly has reached its desiredposition, the system may instruct the treatment unit to treat the targetobject. Also, as described previously, while the vehicle is in motionthe system may instruct the motors to slightly rotate so that the systemmaintains the spraying head assembly targeted at the target object.These micro-adjustments allow the spraying head to stay tracked onto atarget object and account for changes in the pose of the vehicle and/ortreatment.

FIG. 27 is a diagram illustrating pose determination of the agriculturalobservation and treatment system, according to some examples. The figureillustrates an example of a vehicle 1720 with treatment unit 1653attached. A vehicle 1720 is shown moving along a path 2712. For example,as illustrated in F6IG. 24, a vehicle 1720, such as a tractor may beconfigured to tow one or more treatment systems along a vehicle trackhave multiple lanes for the vehicle 1720 and tow support.

If the vehicle 1720 were to remain in a stopped positioned, the system400 could spray the target object 2720 and then move onto the nexttarget object, and then stop and spray the next target object. However,the system 400 is flexibly configured to allow the continuous movementof vehicle 1720 and make adjustments to the position of the sprayinghead of the treatment unit 1653. While the vehicle 1720 is moving alongthe path, the system 400 may determine a pose for the vehicle (e.g.,Vehicle POSE₀, Vehicle POSE₁, Vehicle POSE₂ . . . Vehicle POSE_(n))and/or for the treatment unit 1653 ((e.g., Unit POSE₀, Unit POSE_(n),Unit POSE₂ . . . Unit POSE_(n)). For example, using onboard navigationand IMU sub-systems, the system 400 may determine multiple locations orpositions of the vehicle while the vehicle is moving along the path2712.

As noted above, the treatment unit 1653 emits at fluid at a targetobject 2720. While the vehicle 1720 is moving, the system 400 determinesa translation of Vehicle POSE_(n) and/or the Unit POSE_(n) to a SprayPOSE_(n) such that spraying head is oriented or positioned to allow anemitted projectile fluid to spray upon a desired target object. Forexample, the system 400 may determine that a target object 2720 is to betreated. The system 400 determines a Vehicle POSE₀ and/or a TreatmentUnit POSE₀. The system 400 will provide instructions/signals to themotors of the treatment unit 1653 to adjust one or more axis (e.g.,pitch 2732, yaw 2734 and/or roll) of the spraying head. As the vehicle1720 move along the path 2712, the system 400 periodically determines nposes of the vehicle 1720 and/or the treatment unit 1653. The system 400then translates the periodically determined n poses to an n sprayinghead pose such that the treatment unit may continually spray the targetobject 2720 while the vehicle is moving. The system 100 may evaluatespeed, movement, velocity, direction, altitude, location of the vehicle1720 and/or treatment unit 1653 and determine a pose for the spray head.

As used herein, pose may be understood to be a location and orientationof an object relative to a frame of reference (e.g., x, y, z, phi,theta, psi, where x=an x-axis coordinate in a 3-dimensional coordinatesystem, y=a y-axis coordinate in a 3-dimensional coordinate system, z=az-axis coordinate in a 3-dimensional coordinate system, phi=degree orposition of roll, theta=degree or position of pitch, psi=degree orposition of yaw. For example, the agricultural treatment system maydetermine pitch, roll and yaw values of the vehicle, treatment unit andor the spraying head assembly. In some embodiments, the agriculturaltreatment system may not be configured to identify a pitch, roll and/oryaw of the vehicle, treatment unit and or spraying head. In suchinstances, the value for these variables may be set to zero.

A global frame of reference may be provided for an environment in whichthe agricultural treatment system operates. For example, a global frameof reference may be set to a particular geospatial location or the fixedreference point on a property (e.g., a corner of a barn, structure, a 5g/wifi/gps tower, etc.). The point of reference may be defined as (x=0,y=0, z=0, phi=0, theta=0, psi=0). The agricultural treatment system maydetermine multiple poses of the vehicle, in relation to the point ofreference, as the vehicle moves about the environment. The pose of thevehicle may be defined as vehicle (x_(n), y_(n), z_(n), phi_(n),theta_(n), psi_(n))_(time_interval), the system may determine the nthvalues at a particular time interval being sampled at a particularsample rate (such as 200-5000 times a second). The agriculturaltreatment system may also determine a pose for a treatment unit, such astreatment_unit(x_(n), y_(n), z_(n), phi_(n), theta_(n),psi_(n))_(time_interval). The agricultural treatment system may alsodetermine a pose for a sprayer head of the treatment unit, such asspraying_head (x_(n), y_(n), z_(n), phi_(n), theta_(n),psi_(n))_(time_interval). The sprayer head may have a pose relative tothe vehicle pose, may have a pose relative to the treatment unit poseand/or may have a pose directly in relation to the global frame ofreference. The agricultural treatment system may determine a finalspraying_head (x, y, z, rho, theta, psi) pose to be used to adjust thespraying head to a different position. The final pose can be relative tothe body of the treatment unit, the sprayer apparatus components, thevehicle or components thereof, and/or relative to some (0,0,0,0,0,0)location of the farm.

As described herein, the agricultural treatment system may determine thepose of the vehicle and/or treatment unit and translate the pose intocommands or instructions to adjust a spraying head assembly to emittedfluid at a desired target object. In other words, the agriculturaltreatment system may identify a target object to be sprayed, orient aspraying head assembly to the target object and then control fluidspraying operations to emit fluid from one or more fluid sources at thetarget object. The system can move along a path and make adjustments tothe spraying head assembly such that the fluid is continuously sprayedat the target object and/or detect new target object(s) to be sprayedand then position the spraying head assembly to the detected new targetobject(s).

While the above describes pose determination for a vehicle or treatmentunit, the system may determine a pose for any part or object of thesystem (e.g., a seat, the vehicle, a wheel, treatment unit, sprayinghead, spray box, turret, nozzle tip, etc.). The pose may be determinedwith one or more different sensors (e.g., a camera positioned can obtainimagery of different parts or components), and the system can estimatethe pose of the parts or components. The system may use computer vision,lidar, radar, sonar, GPS, vslam, wheel encoders, motor encoders, IMU,cameras on a spray box. In some embodiments, the system may beconfigured to determine, for example, the vehicle and multiple treatmentunits. This may be done for example where the vehicle is pulling atrailer with many spray boxes places along a frame or support that hasmany wheels. Each of the spray boxes may have different poses due to theruggedness or unevenness of the terrain.

The system may be configured to determine particular poses of thevehicle as a global pose and the treatment units as a local pose. Alocal pose for each treatment unit may be determined in relationship tothe global pose, and/or may be determined individually for the treatmentwithout relationship to the global pose. The system may use the globalpose (a.k.a. vehicle pose) as a localization method to determine itsrelationship to a real-world environment. And the system sensors mayobtain information about the real-world environment. The allows thesystem to build a high map of an agricultural environment (such as afarm). In one embodiment, the system uses a navbox and sensors todetermine the global pose.

The system may use the local pose of a particular component for certainoperations. As discussed herein, the system may determine a pose for atreatment unit and a spraying head. The system would use the local poseof these components to determine its physical relationship as betweenthe component and a target object. For example, two different treatmentunits may each have a spraying head. A first treatment unit and sprayinghead may need to spray a first target object. A second treatment unitand spraying head may need to spray a second target object. In thissituation, the system may determine a pose for each spraying unit andeach of the spraying heads, and then maneuver or orient the spray nozzleof the spraying heads toward their respective target object. In oneembodiment, the system would use the local poses to orient the sprayingheads to emit a projectile fluid at the respective target objects.

In one example, the agricultural treatment system determines multiplevehicle and/or treatment unit poses. The system evaluates a first pose,and then periodically determine subsequent poses. The system maycalculate the difference or changes of the coordinate values from thefirst pose and a subsequently obtained posed. In other words, the systemmay calculate the movement of the vehicle and/or treatment unit. Thecalculated difference or changes then may be translated to a desiredpose for the spraying head. The sample rate of the pose can beconfigured as set rate or a variable rate. For example, the system mayevaluate its pose at predetermined intervals, such as 5 milliseconds. Inan alternate configures, the system may use a variable sample rate suchthat when the vehicle speed increase, the pose determination rateincreases. For example, the sample rate for determining a pose may be 5milliseconds where the vehicle speed is from 1-3 mph, and the samplerate increase to a higher rate, such as every 2 milliseconds, where thevehicle speed is over 3 mph.

In one example, pose for the vehicle may be determined by evaluatingdata from various sources, such as onboard cameras, GPS, IMU's, wheelencoders, steering wheel encoders, LiDAR, RADAR, SONOAR, and additionalsensors that may be used to determine the vehicles position in areal-world environment. The system for example may evaluate the sensordata for example at 200-5000 hz.

In one example, pose for a treatment unit 2800 may be determined insimilar manner to the vehicle, as one or more treatment units would beconfigured in a fixed position in relationship to the vehicle. A changein pose of the vehicle may be considered to be the same change in poseof the treatment unit. The treatment unit may have one or moreprocessors and microcontrollers to monitor and determine the pose of thetreatment. The processors and microcontrollers are configured to keeptrack of treatment unit pose. The treatment unit process periodicallyrequests from a vehicle processor system pose information for thevehicle. The treatment unit process may then determine its pose by usingthe pose of the vehicle and may offset the pose of the vehicle based ona distance value from where a respective treatment unit is positionedrelative to the point of where the pose is determined for the vehicle.As discussed herein, the agricultural treatment system may includemultiple treatment units. Each of the processor of the respectivetreatment unit may determine the pose for the treatment unit. Thus, eachof the treatment units may have a unique pose relative to the determinedvehicle pose.

Each of the treatment unit processors and microcontrollers may determinea spraying head pose. As noted above, each of the treatment unitcontinually poll or request a vehicle pose from the vehicle's computersystem and may determine a treatment unit pose. The treatment unitprocessors are configured to determine and evaluate the positions of themotors via the encoders coupled to the motors. The treatment unitprocesser obtains information from the microcontroller 2875 of thetreatment unit regarding the encoder output. In other words, theencoders provide data output about motors' position and/or rotationalmovement. The microcontroller receives the encoder output data andprovides the data to the treatment unit processor. Similarly, themicrocontroller may receive instructions or data from the treatment unitprocessor, and the microcontroller in turn may provide or translate thereceived instructions or data to instructions, voltage and/or commandsto that cause the motors to rotate in one direction or the other. Theaxial rotation of the motor then causes the linkage assemblies to rotatethereby causing the spraying head to change. The treatment unitprocessor may determine a spraying head pose and provide instructions tothe microcontroller 2875 to then make adjustments to each of the motorssuch the that the spraying head is adjusted to the desired spaying headpose.

In one example, a first 3-dimensional coordinate system may be used forthe vehicle and/or treatment unit pose, and a second 3-dimensionalcoordinate system may be used for the spraying head. Changes in thefirst 3-dimensional coordinate system may be mapped to the second3-dimensional system. Distance moved in the second 3-dimensionalcoordinate system may then be calculated and the distance moved can betranslated into instructions/commands to rotate the motors by a certainamount, degree or time to achieve a desired position of the motor.

While the above discussion, focuses on an example of a single treatmentunit, the system may determine poses for multiple treatment units andadjust the pose of the respective unit spraying head such that each ofthe spraying heads may lock on to their respective target objects.

FIG. 28A-FIG. 28E are block diagram illustrating various configurationsof the system with treatment units 2800 configured for various fluidsource and spraying tip options. Referring to FIG. 28A, the agriculturaltreatment system has onboard circuitry, processors and sensors thatallows the system to obtain imagery of agricultural objects and thenidentify a target object to be sprayed. Furthermore, the agriculturaltreatment system has onboard circuitry, process and sensors that allowsthe system to determine position of the vehicle and/or treatment unit ina three-dimensional space. Moreover, the agricultural treatment systemincludes other cameras and computer vision sensor to obtain and processimagery of external real-world objects 2884. For example, block 2850illustrates a subsystem having a computer unit 2851, communicationchannel 2854, cameras 2853, machine learning model and computer visionalgorithm 2855, lights 2856, and other sensors 2852. For example, thesystem may use GPS location data, IMU data to identify inertial movementand distance moved. Over a period of time, the system may determinemultiple poses of the vehicle and/or treatment unit andconvert/translate these poses that the spraying head would need to bepositioned into such that the spraying head would maintain an emit sprayat the target object while the vehicle is moving.

The subsystem 2850 interacts with a treatment unit 2800. While a singletreatment unit is shown, the subsystem 2850 may interact with andcontrol multiple treatment units. Generally, the treatment unit 2800includes a microcontroller that is operably coupled with one or moresolenoids 2870, pumps, multiple motors 2820, 2830 and multiple encoders2822, 2832. The treatment unit 2800 may draw fluid from one or moresource tanks 2804. The subsystem 2850 may communication viacommunications channel 2842 with another computer system. For example,the subsystem 2850 may receive global registry information and data(e.g., global registry information such as GPS location data, IMU data,VSLAM data, etc.)

The microcontroller 2875 may control or interact with the pump, solenoid2870A, motors 2820, 2830 and encoders 2822, 2832 to position thetreatment head assembly 2860 and emit fluid from one or more fluidsources. For example, based on interaction with the subsystem 2850, thetreatment unit 2800 may control the position of a treatment headassembly 2860 to orient the treatment head assembly 2860 such that thetreatment head assembly 2860 may emit a fluid at a target object 2885.FIG. 28A illustrates a treatment unit with a single fluid source tank2804A and a single solenoid 2870A, and a spraying head 2862A with asingle port.

FIG. 28B illustrates a treatment unit 2800 with multiple fluid sourcetanks 2804A, 2804B, 2804C and 2804D, and multiple solenoids 2870A,2870B, 2870C and 2870D. Fluid may be pumped from the respective fluidsource tanks, through tubing 2806 and emitted via the spraying headassembly 2860 via spraying tip 2862B with a single port.

FIG. 28C illustrates a treatment unit 2800 with multiple fluid sourcetanks 2804A, 2804B, 2804C and 2804D, and multiple solenoids 2870A,2870B, 2870C and 2870D. Fluid may be pumped from the respective fluidsource tanks 2804A, 2804B, 2804C and 2804D and emitted separately viaseparate spraying tip ports on the spraying head 2860 assembly. In theexample, the spraying head has four channels fluid coupled usingseparate tubing to the fluid source tanks 2804A, 2804B, 2804C and 2804D.The microcontroller 2875 may control multiple solenoids 2870A, 2870B,2870C and 2870D to control the release of fluid from the fluid sourcetanks 2804A, 2804B, 2804C and 2804D

FIG. 28D illustrates a treatment unit 2800 with multiple fluid sourcetanks 2804A, 2804B, 2804C and 2804D, and multiple solenoids 2870A,2870B, 2870C and 2870D. Fluid may be pumped from the respective fluidsource tanks 2804A, 2804B, 2804C and 2804D and emitted via singlespraying tip 2862B via the spraying head 2860 assembly. In the example,the spraying head has four channels fluid coupled using separate tubingto the fluid source tanks 2804A, 2804B, 2804C and 2804D. Themicrocontroller 2875 may control multiple solenoids 2870A, 2870B, 2870Cand 2870D to control the release of fluid from the fluid source tanks2804A, 2804B, 2804C and 2804D.

FIG. 28E illustrates and example of the treatment unit 2800 wheremultiple fluid sources may be combined or mixed with a primary fluidsource. The micro controller 2875 may operate a solenoid 2870 to controlthe flow of a primary fluid source, such as water. The primary fluidsource may then be combined with one or more secondary fluid sources2805A, 2805B, 2805C, 2805D. The secondary fluid sources may beconcentrated chemicals or fertilizers that are mixed with the primaryfluid source to dilute the concentrated chemicals. While not shown, eachof the secondary fluid sources 2805A, 2805B, 2805C, 2805D may becontrolled via separate solenoids and pumps to cause the secondary fluidsources to disperse fluid from a tank. The combined mixture of theprimary fluid source and the one or more secondary fluid sources arethen emitted via the spraying head assembly 2860 via spraying tip 2862Ewith a single port

FIG. 28F-FIG. 28H are block diagram illustrating various configurationsof the system with treatment units 2800 configured for emitting laserlight for treatment of agricultural objects. Referring to FIG. 28F, theagricultural treatment system has onboard circuitry, processors andsensors that allows the system to obtain imagery of agricultural objectsand then identify a target object to be sprayed. Furthermore, theagricultural treatment system has onboard circuitry, process and sensorsthat allows the system to determine position of the vehicle and/ortreatment unit in a three-dimensional space. Moreover, the agriculturaltreatment system includes other cameras and computer vision sensor toobtain and process imagery of external real-world objects 2884. Forexample, block 2850 illustrates a subsystem having a computer unit 2851,communication channel 2854, cameras 2853, machine learning model andcomputer vision algorithm 2855, lights 2856, and other sensors 2852. Forexample, the system may use GPS location data, IMU data to identifyinertial movement and distance moved. Over a period of time, the systemmay determine multiple poses of the vehicle and/or treatment unit andconvert/translate these poses that the spraying head would need to bepositioned into such that the spraying head would maintain an emit sprayat the target object while the vehicle is moving.

The subsystem 2850 interacts with a treatment unit 2800. While a singletreatment unit is shown, the subsystem 2850 may interact with andcontrol multiple treatment units. Generally, the treatment unit 2800includes a microcontroller that is operably coupled with multiple motors2820, 2830 and multiple encoders 2822, 2832. The treatment unit 2800 maycause a laser light source 2884 to generate a laser light. The subsystem2850 may communication via communications channel 2842 with anothercomputer system. For example, the subsystem 2850 may receive globalregistry information and data (e.g., global registry information such asGPS location data, IMU data, VSLAM data, etc.).

An example embodiment of the laser may be a femto or laser diode-arraybased laser. In one example, the system uses a laser with a power rangeof 0.5-90 Watts, with an activation speed of 1-5 kHz, with a dwell timeof 1 ns-1S. The frequency may be from Ultraviolet to Infrared. The lasermay be class 2, 3 or 4 with a single to array pulse or continuous wave.The spot size generated by the laser may be from 1 micron to 1 meter indiameter. In another example, the system uses a laser with a power rangeof 0.5-20 Watts, with an activation speed of 10-240 Hz, with a dwelltime of 1 microsecond to 20 milliseconds. The frequency may be in therange of 400-1550 nanometers. The laser may be class 4 pulse orcontinuous wave. The spot size generated by the laser may be adjustable.

While the two example illustrate lasers that may be used by the system,the laser may be configured to operate with different ranges. The lasermay have a power range of 0.5-90 Watts, 0.5-20 Watts or 0.5-5 Watts. Thesystem may activate the laser per second in different ranges, forexample from 1-5000 Hz, 1-240 Hz or 10-40 Hz. The laser may havedifferent dwell time ranges (i.e., how long the laser stays on a singlepulse) such as 1 nanosecond to 1 second, 1 microsecond to 100milliseconds or 5 to 20 milliseconds. The wavelength of the laser may beUV, visible or Infrared. The laser may have a color range of 400-800nanometers or 850-1650 nanometers, or from 435-1550 nanometers. The typeof the laser may be a continuous wave diode laser, pulse diode laser ora laser diode array. The laser may be class 2, 3, 3b or class 4. Thespot size of the laser may be from 1 micron to 1 meter.

FIG. 28F illustrates a treatment unit 2800 with a laser light source2884, an optical control mechanism 2802 (e.g., an optical box), andfiber optic cable 2887 a moveable head assembly 2860 and laser emittingtip 2862A. The agricultural treatment system may generate a laser lightvia the laser light source 2884. The optical control mechanism 2802 mayinclude moveable optical components, such concave and convex lenses, toadjust the intensity, focus and or power of the light received from thelaser light source via the fiber optic cable 2887. The optical controlmechanism 2802 may be part of the moveable head assembly 2860. Thelenses allow for converging and/or diverging of light by shifting thedistance of the lenses to each other thereby adjusting the focus of thelaser light. This adjusted laser light source is then emitted via thelaser emitting tip 2862A. The moveable head assembly is adjustable (asdescribed herein) to orient the laser emitting tip towards anagriculture object such that the emitted laser light may treat anagriculture object. For example, the laser light may photoablate planttissue of an agriculture object. Different laser light sources may besuitable for generating/producing a laser beam to photoablate planttissue.

FIG. 28G illustrates a treatment unit 2800 with a laser light source2884, an optical control mechanism 2802, and a mirror assembly 2888 atreatment head assembly 2860 and laser emitting tip 2862A. The laseroperates similarly as described with respect to FIG. 28F, however,instead of using fiber optic cabling, the treatment unit uses a mirrorassembly to direct the laser light received from the optical controlmechanism 2802, to the laser emitting tip 2862A.

FIG. 28H illustrates a treatment unit 2800 with a laser light source2889, a moveable head assembly 2860 and laser emitting tip 2862A. Inthis example, the laser light source in contained or made part of themoveable head assembly 2860. A small form factor laser light source maybe integrated with the moveable head assembly 2860.

FIG. 29 illustrates example implementations of method 2900 that may beperformed by some example systems described above including system 100,agricultural treatment system 400, system 600, and system 800. Forexample, in one mode of operation, at step 2910, the agriculturaltreatment system determines a first pose of a treatment unit or vehicle.The determination of the pose is described above as to FIGS. 27 and 28 .At step 2920, the agricultural treatment system, translates the firstpose of the of the treatment unit or vehicle, and determines a firstpose of a spraying head of the treatment unit. At step 2930, theagricultural treatment system adjusts the spraying head position basedon the determined first pose of the spraying head. The spraying head,for example, may be repositioned by instructing one or more motors ofthe agricultural treatment system to rotate, thereby causing thespraying head to pivot or rotate along one or more axis. At step 2940,the agricultural treatment system controls a fluid flow regulator (suchas a solenoid or another control device) to allow fluid to be emittedfrom the spraying head at a target object. The vehicle may move along apath, and while doing so the agricultural treatment system mayperiodically determine n poses. At step 2950, the agricultural treatmentsystem determines an nth pose of the treatment unit and/or vehicle whilethe vehicle moves along a path and determines a nth pose for thespraying head. At step 2960, the agricultural treatment system adjuststhe spraying head position based on the determined nth pose of thespraying head. Steps 2940 through 2970 may be repeated. The periodicpose determine process allows the agricultural treatment system tocontinually adjust the spraying head while the vehicle is moving so atto maintain the emitted spray at the target object.

While the agricultural treatment system moves along a path, the systemmay continuously evaluate for additional target objects to be sprayedusing one or more treatment units. As described above, the spraying headassembly may be positioned such that a fluid may be emitted at anidentified target object. After a target object is sprayed with a fluid,the system may instruct the spraying head assembly to reposition to aready position, such as the neutral position of x=0, y=0, or at someother ready position. For example, while the vehicle is moving forward aspraying head assembly may be pointed towards the forward path ofmovement. Doing so would allow (i.e., get ready) the spraying headassembly to be in a ready position when a new target object is detected.The system may instruct the treatment unit spraying head assembly tomove into the ready position when the system is initially powered on.Moreover, the system may instruct a particular treatment unit sprayinghead assembly to move into the ready position after the spraying of athen current target object is completed. Moving the spraying headassembly into a forward ready position allows the agricultural treatmentsystem to readily start spraying subsequent target objects as soon asthey are detected without first having to move the spraying head to thetarget object.

In one example, the treatment unit 2500, may have a high-powered laserunit or laser chip embedded in or supported by the treatment unit 2500,can be configured to treat portions of plants that are larger than planttypically only grow a few inches or feet above the ground. These plantscan include trees, orchard trees, or other plants with one or moretrunks, shrubs, bushes, or other plants grown on trellises or otherhuman made mechanisms such that a horizontally or top mounted treatmentunit 2500 is more practical rather than a treatment unit substantiallypointing at the ground with rotational freedom.

While the above disclosure contemplates the control of a spraying headassembly for the emission of a projectile fluid, the spraying headassembly may be replaced with a controllable laser head assembly. Also,a laser source may be attached to the spraying head assembly. The systemmay control the positioning of a laser head assembly to position thelaser head assembly to direct an emitted laser beam at a target object.The laser beam may be used, for example, to ablate, burn or otherwisetreat the target object with a laser light beam. Additionally, differentlaser beams of different wave lengths may be configured on the laserhead assembly. The laser light may be focused to a desired diameter totreat a target object. In one embodiment, the spraying head includes aspraying nozzle and a laser emitting tip and may be disposed next toeach other such that either a laser or a spray nozzle can activate upontargeting an object of interest for treatment.

The system may treat the target object based on the identified targetobject. For example, the system may set operative parameters of thelaser to treat the target object (such as duration, frequency,wavelength, laser pulse repetition, etc.). Different target objects maybe treated with different parameters using emitted laser light from thetreatment head.

In one embodiment, the agricultural treatment system may be configuredto monitor the health of the spraying head and determine whether thespraying head is accurately emitting a fluid at a target object. In someinstances, the spraying tip may build up residue or other particulate.For example, the spraying head may disperse a fluid containing asolution of salts or of other compounds. Over time, salts or othercompounds from the solutions may build up on the outer surface of thespraying head tip and cause an emitted fluid to deviate from an intendedprojected course. In other words, the emitted fluid may miss an intendedtarget object if the emitted fluid deviates in its projected direction.

The system may correct for a deviation of the projected fluid byadjusting the spraying head to account for the deviation. As the fluidis emitted from the spraying head, an onboard camera may obtain imageryof the fluid as the fluid is emitted or projected at an intended targetobject. The system may determine whether or not the intended targetobject was actually sprayed by the emitted fluid. The system maycalculate an adjustment by determining a distance and position of wherethe emitted fluid was actually sent, and where the fluid should havelanded on the target object. The system then can determine an offset tomake a spraying head positional adjustment such that subsequent emittedfluids would land at an intended location of the target object.

In one mode, the system may continuously emit fluid in a spray or inbursts of fluid, and then determine the location of where the fluid isprojected. The system may make slight or micro adjustments to theposition of the spraying head assembly until the emitted fluid issprayed at the target object at an intended location. The positionaladjustment values then may be used as an offset for subsequent spraying.For example, an emitted spray may be spraying 1.5 inches to the left ofan intended location of a target object. The system can then move thespraying head towards the right of the target object and determine whenan emitted projectile fluid accurately hits the target object. Thisallows the system to determine what position or distance the sprayinghead needs to move to correct for spraying location error.

In one example, the system may use computer vision to track a targetobject while the vehicle is in motion. The system may evaluate imageryof a target object with onboard cameras. The system may determine theposition of features or objects in an image and evaluate the positionalchanges of pixels of the object moving in the image. The system maytranslate the pixel movement to adjustments to the spraying headassembly such that the system adjusts the spraying head assembly so thatthe treatment unit accurately emits a fluid at the target object.

This process of correcting for spraying head projectile deviation mayalso be used when a new spraying head tip is attached the spraying headassembly. This process allows for initial configuration of a treatmentunit to identify and correct for any deviation of an emitted fluid fromthe spraying tip.

FIG. 30 illustrates example implementations of method 3000 that may beperformed by some example systems described above including system 100,agricultural treatment system 400, system 600, and system 800. At step3010, the agricultural treatment system determines a relative locationof a target object. At step 3020, the system may emit a fluid at thetarget object via a treatment unit. At step 3030, the system monitorsand tracks the fluid emitted at the target object. At step 3040, thesystem determines whether the emitted fluid sprayed at the target objectat an intended location. If yes, then at step 3044, the systemdetermines a relative location of a second target object and continuesto step 3020. If no, then at step 3050, the system determines an offsetfor the position and/or orientation of a spraying head. Next at step3060, the system determines a second relative location of the firsttarget object. Then at step 3070, the system positions and/or orientsthe spraying head, in part, using the offset, to target the first targetobject.

For example, the agricultural treatment system may determine a firsttarget object to be sprayed. The treatment unit emits a fluid at thefirst target object via a spraying head. The system may use an onboardcomputer vision system to monitor the emitted fluid at the targetobject. The system determines whether the emitted fluid sprayed thetarget object at an intended location. The system may evaluate obtaineddigital images and identify whether or not the emitted fluid actuallysprayed the target object. The system may also determine at whatdistance and location the projectile stream deviated from the targetobject. The system may determine an offset for the position of thespraying head. For example, the system may calculate a positionaladjustment to the spraying head so that the spraying head would spraythe fluid at an intended target object. The system then may spraysubsequent target objects. The system may determine a second targetobject to be sprayed. The system may then emit a fluid at the secondtarget object via the spraying head using the offset. For example, thespraying head would be positioned in part using the determined offset.

FIG. 31 illustrates example implementations of method 2600 that may beperformed by some example systems described above including system 100,agricultural treatment system 400, system 600, and system 800. Theagricultural treatment system may identify and determine that multipletargets are in close proximity to one another and a particular targetobject can be treated while the spraying head assembly is positioned toemit fluids toward one of the target objects. The treatment unit may beconfigured such that system may emit a fluid from one source tank at afirst target object, and then emit a second fluid from another sourcetank. At step 3110, the system determines a first target object and asecond target object for treatment. At step 3120, the system determinesthat the first target object is a first type of a target object, and thesecond target object is of a second type of a target object. Forexample, the system may recognize the particular type of a target objectusing various computer vision and object detection techniques. At step3130, based on the first determined object type, the system may treatthe first target object with a first treatment from a first fluidsource. The system may cause fluid from a first source tank to beemitted at the first target object. At step 3140, based on the seconddetermined object type, treat the second target object with a secondtreatment from a second fluid source. For example, two target objectsmay be identified being close in proximately to one another.

FIG. 32 illustrates example implementations of method 3200 that may beperformed by some embodiments of the systems described above includingsystem 100, agricultural treatment system 400, system 600, and system800. A treatment unit may pump fluid from different or multiple tanksources and treat a target object by emitting fluid from the differenttank sources. For example, it may be desirable in some instances, totreat a particular type of a target object, such as a bud or a flower,with fluid from multiple tank sources, whereas a further developedagricultural object, may only need treatment from one of the tanksources. At step 3210, the system may determine a first target objectfor treatment. As discussed herein, the system may identify a targetobject to be treated. At step 3220, based on a first determined targetobject, the system may select from two or more fluid sources to treatthe target object. As discussed herein, the system may include multipletanks or containers of different fluids that may be used to treat atarget object. The treatment unit may be configured to cause fluidpumped from the multiple tanks to be mixed together and emitted from asingle spraying tip port, pumped separately and emitted sequentiallyfrom one source tank and then another, or may be emitted from a tip thathas multiple spraying tip ports (e.g., a 4-port spraying tip). At step3230, the treatment unit emit a first fluid at the determined targetobject from a first fluid source. At step 3240, the treatment unit emitsa second fluid at the determined target object from the second fluidsource. The system may determine that the target object is of aparticular type of object, and the select from one or more source tanksto pump fluid and then treat the target object with the fluid.

FIG. 33 illustrates example implementations of method 3300 that may beperformed by some embodiments of the systems described above includingsystem 100, agricultural treatment system 400, system 600, and system800. At step 3310, the system accesses an image of an agricultural scenehaving a plurality of objects. At step 3320, the system detects aplurality of target objects in the real world based on an objectdetection and localization in the image. At step, 3340 the systemdetermines a fluid profile for treating the first target object. At step3350, the system sends instructions of a first treatment parameter to atreatment unit 3350. At step 3360, the system activates the treatmentunit to emit a fluid projectile at the first target object. At step3370, the system identifies and tracks a second target object in thereal world. At step 3380, the system determines a second fluid profilefor treating the second target object. As step 3390, the system sendsinstructions of a second treatment parameter to the treatment unit. Atstep 3392, the system activates the treatment unit to emit a secondfluid projectile at the second target object.

FIG. 34 illustrates example implementations of method 3400 that may beperformed by some embodiments of the systems described above includingsystem 100, agricultural treatment system 400, system 600, and system800. At step 3420, the system identifies and tracks a first targetobject in the real world. At step 3430, the system determines a firstdesired spot size for treating the first target object. At step 3440,the system determines a fluid profile for the first desired spot size.At step 3450, the system sends instructions of a first treatmentparameter for the fluid profile to a treatment unit. At step 3460, thesystem determines a first fluid pressure against the treatment unit. Atstep 3470, the system sends instructions based on the first pressure toactivate a solenoid to allow release of a first fluid projectile. Atstep 3480, the system orients treatment unit activate the solenoid. Atstep 3490, the system determines a spray profile associated with thefirst fluid projectile. The following further describes theseoperations.

As discussed above, the system may use a pump to create pressurizedfluid from the pump to a solenoid. The system may send specific voltageand pressure instructions to the solenoid such that, accounting for thedistance between the turret nozzle to the surface of the target, a ⅛inch to 5 inch diameter of the spot of the spray can hit the target.Moreover, the system may variably and incrementally change the liquidprojectile for every spray. The system may utilize a predetermined basepressure from the pump to the solenoid, and then open and close thesolenoid by providing voltage instructions (for example, 24 or 48volts). The spot of the spray of the emitted projectile may becontrolled by the system to achieve a desired spray amount and spraydiameter to cover an area of a target object.

While the solenoid is completely closed, the fluid may be pressurized toa particular psi. For example, a pump may operate to pressurize thefluid in the range of 1-200 psi. A working line psi from the pump to thesolenoid may be about 60 psi when the solenoid is completely closed. Anemission tubing from the solenoid to the nozzle and/or spray tip wouldhave a psi less than the working line psi when the solenoid is closed.The opening of the solenoid releases the pressurized fluid from theworking line and causes the fluid to fill into the emission line and outthrough the nozzle and/or spray tip. Over a period of time, the emissionline may build up pressure and cause the pressurized fluid to emitthrough the nozzle and/or spray tip. By quickly opening and closing thesolenoid, the system may emit intermittent bursts of the fluid from theworking line causing the fluid to emit as a projectile via the nozzleand/or spray tip.

As fluid leaves the pressurized working line behind the solenoid, thepressurized working line behind the solenoid (from the pump to thesolenoid) loses a small, but negligible amount of pressure. As more andmore fluid leaves the working line (for example, 100 bursts or shots),the overall drop in pressure becomes nontrivial. In certain situations,the pump may not be able to compensate for the drop in pressure until apressure drop becomes significant enough. For example, the pressure maybe 60 psi up to the wall of the solenoid. As the treatment unit emits100 bursts of fluid projectiles, the pressure behind the solenoid mayincrementally drop, for example, to 40 psi. At 40 psi, a pressure sensormay inform the pump and solenoid to accommodate for the pressure drop,and the system will increase pressure back to 60 (and in some instancesmaybe increase the psi in the working line to more than 60 psi toquickly reach a base 60 psi).

A situation may occur in that dropping the pressure in the working linefrom 60 psi to the 40 psi, and then suddenly increasing back to 60 psi,incremental bursts (e.g., shots) may be emitted at slightly differentstarting pressures. So if the system opens the solenoid at the same rateand same voltage every time, the solenoid actually may open at differentrates (because the pressure pushing at the wall was different), soemitted fluid projectiles are incrementally different. To account forthe variability of the pressure in the working line, the system mayaccount for the incremental drop in pressure due. The system maygenerate instructions to change voltages sent to the solenoid so tomaintain the same droplet size and/or fluid volume. The amount by whichthe solenoid opens each time may be slightly different so as to maintainthe same trajectory, volume and/or droplet for the size emitted fluidprojectile (thereby accounted for the differences in the psi for eachburst or shot of the fluid because the pressure pushing at the wall isdifferent). The system may be calibrated and/or configured to open andclose the solenoid at different time intervals and different timedurations such that the amount of pressure is the same for each shot.Alternatively, the system may be calibrated to open and close atdifferent time intervals and for different durations such that theamount of pressure is similar. The system may use a spray profile thatcontrols the timing and duration of the opening and closing of thesolenoid.

For example, the system may receive one or more images of anagricultural scene having one or more agricultural objects, such asplant object. The system may then detect a plurality of target objectsbased on the received one or more images. The system may identify afirst target object in the real world from the detecting of theplurality of target objects in the received one or more images. Thesystem may determine a set of first treatment parameters (e.g., sprayprofile) for the first target object. Based on the first set oftreatment parameters, the system may instruct a treatment unit to emit afluid projectile at the first target object. The first set of treatmentparameters may include one or more of: a spray speed for an emittedfluid, a spray size for an emitted fluid, a spat profile and/or a sprayduration. Based on the spray profile, the system controls a solenoid torelease a pressurized fluid that has been pumped from a fluid source,the emitted fluid projectile that includes a portion of the releasedpressurized fluid. The system may adjust the opening and closing of thesolenoid to account for hysteresis band of a pressure drop in thepressurized fluid.

The system may continue to treat multiple other target objects, such asagricultural objects that are plant objects. The system may identify asecond target object in the real world in the received one or moreimages. The identification of the other target objects may occur in realtime concurrently or after the first target object is detected. Thesystem may determine a set of second treatment parameters (e.g., a sprayprofile) for a second target object. The set of second treatmentparameters may bet the same or different from the first treatmentparameters for the first target object. For example, the system maydetermine a second spray size for the second target object that isdifferent from a spray size for the first target object. The system maydetermine a second spray speed for the second target object that isdifferent from a spray speed for the first target object. The system maydetermine a second spray volume for the second target object that isdifferent from a spray volume for the first target object. Also, thesystem may determine a target spat size for the second target objectthat is different from a target splat size for the first target object.For example, a spat size of a same about of a drop of fluid may have adifferent impact shape when it impacts a surface. The impact shape maybe changed based on the trajectory speed of the drop of fluid when itimpacts the surface of the target.

After the second set of parameters are determined, then the system mayactivate the treatment unit to emit a second fluid projectile at thesecond target object based on the second treatment parameters. Thesystem may confirm whether the second fluid projectile contacted thesecond target object (e.g., based on sensor data comprising digitalimagery, lidar data, sonar data, radar data or a combination thereof).

While the foregoing describes examples of the pressurized fluid spraywith particular pressure and spray profiles, the system may beconfigured within various ranges. For example, the emitted spray may beemitted from about 1 millisecond to 1 second between the range of 40 to80 psi. The system may be capable of generating a pressure of about 1psi to 2700 psi in the working line. The volume of fluid released andemitted may be from about 1 milliliter to about 1000 milliliters. Thetarget splat diameter can be about a dime size at 40 psi to about aquarter size at 60 psi. The projectile/droplet size may be about 1millimeter to about 100 millimeters diameter in a single drop (notvolumetric).

The terminology used herein is for the purpose of describing particularaspects only and is not intended to be limiting of the disclosure. Asused herein, the singular forms “a”, “an”, and “the” are intended tocomprise the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”,“comprising”, or a combination thereof, when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

While the disclosure has been particularly shown and described withreference to specific examples thereof, it should be understood thatchanges in the form and details of the disclosed examples may be madewithout departing from the scope of the disclosure. Although variousadvantages, aspects, and objects of the present disclosure have beendiscussed herein with reference to various examples, it will beunderstood that the scope of the disclosure should not be limited byreference to such advantages, aspects, and objects. Rather, the scope ofthe disclosure should be determined with reference to the claims.

Some portions of the preceding detailed descriptions have been presentedin terms of algorithms and symbolic representations of operations ondata bits within a computer memory. These algorithmic descriptions andrepresentations are the ways used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as “identifying” or “determining” or “executing” or“performing” or “collecting” or “creating” or “sending” or the like,refer to the action and processes of a computer system, or similarelectronic computing device, that manipulates and transforms datarepresented as physical (electronic) quantities within the computersystem's registers and memories into other data similarly represented asphysical quantities within the computer system memories or registers orother such information storage devices.

The present disclosure also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for theintended purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, any type of disk,optical disks, CD-ROMs, and magnetic-optical disks, read-only memories(ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic oroptical cards, solid state drives, flash drives, SATA drives, NAND and3D NAND flash drives, or any type of media suitable for storingelectronic instructions, each coupled to a computer system bus.

Various general-purpose systems may be used with programs in accordancewith the teachings herein, or it may prove convenient to construct amore specialized apparatus to perform the method. The structure for avariety of these systems will appear as set forth in the descriptionabove. In addition, the present disclosure is not described withreference to any particular programming language. It will be appreciatedthat a variety of programming languages may be used to implement theteachings of the disclosure as described herein.

The present disclosure may be provided as a computer program product, orsoftware, that may include a machine-readable medium having storedthereon instructions, which may be used to program a computer system (orother electronic devices) to perform a process according to the presentdisclosure. A machine-readable medium includes any mechanism for storinginformation in a form readable by a machine (e.g., a computer). Forexample, a machine-readable (e.g., computer-readable) medium includes amachine (e.g., a computer) readable storage medium such as a read onlymemory (“ROM”), random access memory (“RAM”), magnetic disk storagemedia, optical storage media, flash memory devices, etc.

In the foregoing disclosure, implementations of the disclosure have beendescribed with reference to specific example implementations thereof. Itwill be evident that various modifications may be made thereto withoutdeparting from the broader spirit and scope of implementations of thedisclosure as set forth in the following claims. The disclosure anddrawings are, accordingly, to be regarded in an illustrative senserather than a restrictive sense.

What is claimed is:
 1. A treatment apparatus comprising: at least aportion of a laser treatment head assembly comprising a moveabletreatment head; a first motor assembly comprising a first motor, thefirst motor assembly rotatable in a first rotational axis; a firstlinkage assembly connected to the first motor and the treatment headassembly, wherein the treatment head is moveable by rotation of thefirst motor, the first linkage assembly comprising a surface portion,wherein the surface portion limits a degree of rotation of the firstmotor, the first linkage assembly comprising a first leg having an innerside and a second leg having an inner side, wherein the inner sideslimit a degree of rotation of the first motor by connecting with a post,wherein the surface portion is the inner side; a second motor assemblycomprising a second motor, the second motor rotatable in a secondrotational axis, the first rotational axis being different than secondrotational axis; and a second linkage assembly connected to the secondmotor and the treatment head assembly, wherein the treatment head ismoveable by rotation of the second motor.
 2. The treatment apparatus ofclaim 1, further comprising: a laser light source; and a fiber opticcable configured for receiving generated laser light from the laserlight source, wherein the fiber optic cable is coupled to a laseremitting tip.
 3. The treatment apparatus of claim 2, further comprising:a controller configured to control the first and second motors therebyadjusting a position of the moveable treatment head.
 4. The treatmentapparatus of claim 1, wherein rotation of the first motor causes thetreatment head assembly to pivot along a first axis, and rotation of thesecond motor causes the treatment head assembly to pivot along a secondaxis.
 5. The treatment apparatus of claim 1, wherein the first linkageassembly comprises a first set of two linkage arms, and the secondlinkage assembly comprises a second set of two linkage arms.
 6. Thetreatment apparatus of claim 1, wherein the first linkage assemblycomprises a frame having a first opening and a second opening forattachment to the treatment head assembly, and the second linkageassembly comprises a first arm and a second arm, wherein the first armis rotatably attached to the second arm and the first arm has an openingfor attachment to the treatment head assembly.
 7. The treatmentapparatus of claim 1, wherein the first motor and second motor areoperable such that the first motor rotates in a first plane and thesecond motor rotates in a second plane, the first plane beingperpendicular to the second plane.
 8. The treatment apparatus of claim1, wherein the first motor and second motor are operable that while thetreatment head assembly is in a neutral position a line extending fromthe center of the first motor would intersect the rotational axis of thetreatment head assembly along one axis, and a line extending from thecenter of the second motor would intersect the rotational axis of thetreatment head assembly along another axis.
 9. The treatment apparatusof claim 3, further comprising: a first encoder coupled to the firstmotor; a second encoder coupled to the second motor; and themicrocontroller operably coupled to the first and second motors, andoperably coupled to the first and second encoders.
 10. A treatmentapparatus comprising: at least a portion of a laser treatment headassembly comprising a moveable treatment head; a first motor assemblycomprising a first motor, the first motor assembly rotatable in a firstrotational axis; a first linkage assembly connected to the first motorand the treatment head assembly, wherein the treatment head is moveableby rotation of the first motor; a second motor assembly comprising asecond motor, the second motor rotatable in a second rotational axis,the first rotational axis being different than second rotational axis; asecond linkage assembly connected to the second motor and the treatmenthead assembly, wherein the treatment head is moveable by rotation of thesecond motor; and wherein the first linkage assembly comprises a framehaving a first opening and a second opening for attachment to thetreatment head assembly, and the second linkage assembly comprises afirst arm and a second arm, wherein the first arm is rotatably attachedto the second arm and the first arm has an opening for attachment to thetreatment head assembly.
 11. The treatment apparatus of claim 10,further comprising: a laser light source; and a fiber optic cableconfigured for receiving generated laser light from the laser lightsource, wherein the fiber optic cable is coupled to a laser emittingtip.
 12. The treatment apparatus of claim 11, further comprising: acontroller configured to control the first and second motors therebyadjusting a position of the moveable treatment head.
 13. The treatmentapparatus of claim 10, wherein rotation of the first motor causes thetreatment head assembly to pivot along a first axis, and rotation of thesecond motor causes the treatment head assembly to pivot along a secondaxis.
 14. The treatment apparatus of claim 10, the first linkageassembly comprising a first leg having an inner side and a second leghaving an inner side, wherein the inner sides limit a degree of rotationof the first motor by connecting with a post.
 15. The treatmentapparatus of claim 10, wherein the first linkage assembly comprises afirst set of two linkage arms, and the second linkage assembly comprisesa second set of two linkage arms.
 16. The treatment apparatus of claim10, wherein the first motor and second motor are operable such that thefirst motor rotates in a first plane and the second motor rotates in asecond plane, the first plane being perpendicular to the second plane.17. The treatment apparatus of claim 10, wherein the first motor andsecond motor are operable that while the treatment head assembly is in aneutral position a line extending from the center of the first motorwould intersect the rotational axis of the treatment head assembly alongone axis, and a line extending from the center of the second motor wouldintersect the rotational axis of the treatment head assembly alonganother axis.
 18. The treatment apparatus of claim 12, furthercomprising: a first encoder coupled to the first motor; a second encodercoupled to the second motor; and the microcontroller operably coupled tothe first and second motors, and operably coupled to the first andsecond encoders.
 19. A treatment apparatus comprising: at least aportion of a laser treatment head assembly comprising a moveabletreatment head; a first motor assembly comprising a first motor, thefirst motor assembly rotatable in a first rotational axis; a firstlinkage assembly connected to the first motor and the treatment headassembly, wherein the treatment head is moveable by rotation of thefirst motor, the first linkage assembly comprising a surface portion,wherein the surface portion limits a degree of rotation of the firstmotor; a second motor assembly comprising a second motor, the secondmotor rotatable in a second rotational axis, the first rotational axisbeing different than second rotational axis; and a second linkageassembly connected to the second motor and the treatment head assembly,wherein the treatment head is moveable by rotation of the second motor,wherein the first linkage assembly comprises a frame having a firstopening and a second opening for attachment to the treatment headassembly, and the second linkage assembly comprises a first arm and asecond arm, wherein the first arm is rotatably attached to the secondarm and the first arm has an opening for attachment to the treatmenthead assembly.